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Systematic integration of biomedical knowledge prioritizes drugs for repurposing

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This manuscript was automatically generated from dhimmel/rephetio-manuscript@7b5b0e6 on October 3, 2017.

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This manuscript (permalink) was automatically generated from dhimmel/rephetio-manuscript@1ddd48d on March 14, 2019.

Authors

Abstract

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Introduction

The cost of developing a new therapeutic drug has been estimated at 1.4 billion dollars [1], the process typically takes 15 years from lead compound to market [2], and the likelihood of success is stunningly low [3]. Strikingly, the costs have been doubling every 9 years since 1970, a sort of inverse Moore’s law, which is far from an optimal strategy from both a business and public health perspective [4]. Drug repurposing — identifying novel uses for existing therapeutics — can drastically reduce the duration, failure rates, and costs of approval [5]. These benefits stem from the rich preexisting information on approved drugs, including extensive toxicology profiling performed during development, preclinical models, clinical trials, and postmarketing surveillance.

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Drug repurposing is poised to become more efficient as mining of electronic health records (EHRs) to retrospectively assess the effect of drugs gains feasibility [6–9]. However, systematic approaches to repurpose drugs based on mining EHRs alone will likely lack power due to multiple testing. Similar to the approach followed to increase the power of genome-wide association studies (GWAS) [10,11], integration of biological knowledge to prioritize drug repurposing will help overcome limited EHR sample size and data quality.

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In addition to repurposing, several other paradigm shifts in drug development have been proposed to improve efficiency. Since small molecules tend to bind to many targets, polypharmacology aims to find synergy in the multiple effects of a drug [12]. Network pharmacology assumes diseases consist of a multitude of molecular alterations resulting in a robust disease state. Network pharmacology seeks to uncover multiple points of intervention into a specific pathophysiological state that together rehabilitate an otherwise resilient disease process [13,14]. Although target-centric drug discovery has dominated the field for decades, phenotypic screens have more recently resulted in a comparatively higher number of first-in-class small molecules [15]. Recent technological advances have enabled a new paradigm in which mid- to high-throughput assessment of intermediate phenotypes, such as the molecular response to drugs, is replacing the classic target discovery approach [16–18]. Furthermore, integration of multiple channels of evidence, particularly diverse types of data, can overcome the limitations and weak performance inherent to data of a single domain [19]. Modern computational approaches offer a convenient platform to tie these developments together as the reduced cost and increased velocity of in silico experimentation massively lowers the barriers to entry and price of failure [20,21].

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Drug repurposing is poised to become more efficient as mining of electronic health records (EHRs) to retrospectively assess the effect of drugs gains feasibility [6,7,8,9]. However, systematic approaches to repurpose drugs based on mining EHRs alone will likely lack power due to multiple testing. Similar to the approach followed to increase the power of genome-wide association studies (GWAS) [10,11], integration of biological knowledge to prioritize drug repurposing will help overcome limited EHR sample size and data quality.

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In addition to repurposing, several other paradigm shifts in drug development have been proposed to improve efficiency. Since small molecules tend to bind to many targets, polypharmacology aims to find synergy in the multiple effects of a drug [12]. Network pharmacology assumes diseases consist of a multitude of molecular alterations resulting in a robust disease state. Network pharmacology seeks to uncover multiple points of intervention into a specific pathophysiological state that together rehabilitate an otherwise resilient disease process [13,14]. Although target-centric drug discovery has dominated the field for decades, phenotypic screens have more recently resulted in a comparatively higher number of first-in-class small molecules [15]. Recent technological advances have enabled a new paradigm in which mid- to high-throughput assessment of intermediate phenotypes, such as the molecular response to drugs, is replacing the classic target discovery approach [16,17,18]. Furthermore, integration of multiple channels of evidence, particularly diverse types of data, can overcome the limitations and weak performance inherent to data of a single domain [19]. Modern computational approaches offer a convenient platform to tie these developments together as the reduced cost and increased velocity of in silico experimentation massively lowers the barriers to entry and price of failure [20,21].

Hetnets (short for heterogeneous networks) are networks with multiple types of nodes and relationships. They offer an intuitive, versatile, and powerful structure for data integration by aggregating graphs for each relationship type onto common nodes. In this study, we developed a hetnet (Hetionet v1.0) by integrating knowledge and experimental findings from decades of biomedical research spanning millions of publications. We adapted an algorithm originally developed for social network analysis and applied it to Hetionet v1.0 to identify patterns of efficacy and predict new uses for drugs. The algorithm performs edge prediction through a machine learning framework that accommodates the breadth and depth of information contained in Hetionet v1.0 [22,23]. Our approach represents an in silico implementation of network pharmacology that natively incorporates polypharmacology and high-throughput phenotypic screening.

One fundamental characteristic of our method is that it learns and evaluates itself on existing medical indications (i.e. a “gold standard”). Next, we introduce previous approaches that also performed comprehensive evaluation on existing treatments. A 2011 study, named PREDICT, compiled 1,933 treatments between 593 drugs and 313 diseases [24]. Starting from the premise that similar drugs treat similar diseases, PREDICT trained a classifier that incorporates 5 types of drug-drug and 2 types of disease-disease similarity. A 2014 study compiled 890 treatments between 152 drugs and 145 diseases with transcriptional signatures [25]. The authors found that compounds triggering an opposing transcriptional response to the disease were more likely to be treatments, although this effect was weak and limited to cancers. A 2016 study compiled 402 treatments between 238 drugs and 78 diseases and used a single proximity score — the average shortest path distance between a drug’s targets and disease’s associated proteins on the interactome — as a classifier [26].

We build on these successes by creating a framework for incorporating the effects of any biological relationship into the prediction of whether a drug treats a disease. By doing this, we were able to capture a multitude of effects that have been suggested as influential for drug repurposing including drug-drug similarity [24,27], disease-disease similarity [24,28], transcriptional signatures [17,18,25,29,30], protein interactions [26], genetic association [31,32], drug side effects [33,34], disease symptoms [35], and molecular pathways [36]. Our ability to create such an integrative model of drug efficacy relies on the hetnet data structure to unite diverse information. On Hetionet v1.0, our algorithm learns which types of compound–disease paths discriminate treatments from non-treatments in order to predict the probability that a compound treats a disease.

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Results

Hetionet v1.0

We obtained and integrated data from 29 publicly available resources to create Hetionet v1.0 (Figure 1). The hetnet contains 47,031 nodes of 11 types (Table 1) and 2,250,197 relationships of 24 types (Table 2). The nodes consist of 1,552 small molecule compounds and 137 complex diseases, as well as genes, anatomies, pathways, biological processes, molecular functions, cellular components, perturbations, pharmacologic classes, drug side effects, and disease symptoms. The edges represent relationships between these nodes and encompass the collective knowledge produced by millions of studies over the last half century.

-Figure 1: Hetionet v1.0. A) The metagraph, a schema of the network types. B) The hetnet visualized. Nodes are drawn as dots and laid out orbitally, thus forming circles. Edges are colored by type. C) Metapath counts by path length. The number of different types of paths of a given length that connect two node types is shown. For example, the top-left tile in the Length 1 panel denotes that Anatomy nodes are not connected to themselves (i.e. no edges connect nodes of this type between themselves). However, the bottom-left tile of the Length 4 panel denotes that 88 types of length-four paths connect Symptom to Anatomy nodes.
Figure 1: Hetionet v1.0. A) The metagraph, a schema of the network types. B) The hetnet visualized. Nodes are drawn as dots and laid out orbitally, thus forming circles. Edges are colored by type. C) Metapath counts by path length. The number of different types of paths of a given length that connect two node types is shown. For example, the top-left tile in the Length 1 panel denotes that Anatomy nodes are not connected to themselves (i.e. no edges connect nodes of this type between themselves). However, the bottom-left tile of the Length 4 panel denotes that 88 types of length-four paths connect Symptom to Anatomy nodes.
+Figure 1: Hetionet v1.0. A) The metagraph, a schema of the network types. B) The hetnet visualized. Nodes are drawn as dots and laid out orbitally, thus forming circles. Edges are colored by type. C) Metapath counts by path length. The number of different types of paths of a given length that connect two node types is shown. For example, the top-left tile in the Length 1 panel denotes that Anatomy nodes are not connected to themselves (i.e. no edges connect nodes of this type between themselves). However, the bottom-left tile of the Length 4 panel denotes that 88 types of length-four paths connect Symptom to Anatomy nodes.
Figure 1: Hetionet v1.0. A) The metagraph, a schema of the network types. B) The hetnet visualized. Nodes are drawn as dots and laid out orbitally, thus forming circles. Edges are colored by type. C) Metapath counts by path length. The number of different types of paths of a given length that connect two node types is shown. For example, the top-left tile in the Length 1 panel denotes that Anatomy nodes are not connected to themselves (i.e. no edges connect nodes of this type between themselves). However, the bottom-left tile of the Length 4 panel denotes that 88 types of length-four paths connect Symptom to Anatomy nodes.

For example, Compound–binds–Gene edges represent when a compound binds to a protein encoded by a gene. This information has been extracted from the literature by human curators and compiled into databases such as DrugBank, ChEMBL, DrugCentral, and BindingDB. We combined these databases to create 11,571 binding edges between 1,389 compounds and 1,689 genes. These edges were compiled from 10,646 distinct publications, which Hetionet binding edges reference as an attribute. Binding edges represent a comprehensive catalog constructed from low throughput experimentation. However, we also integrated findings from high throughput technologies — many of which have only recently become available. For example, we generated consensus transcriptional signatures for compounds in LINCS L1000 and diseases in STARGEO.

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Hetionet v1.0

Table 1: Metanodes. Hetionet v1.0 includes 11 node types (metanodes). For each metanode, this table shows the abbreviation, number of nodes, number of nodes without any edges, and the number of metaedges connecting the metanode. Table 1: Metanodes. Hetionet v1.0 includes 11 node types (metanodes). For each metanode, this table shows the abbreviation, number of nodes, number of nodes without any edges, and the number of metaedges connecting the metanode.
Metanode
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Systematic mechanisms of efficacy

One aim of Project Rephetio was to systematically evaluate how drugs exert their therapeutic potential. To address this question, we compiled a gold standard of 755 disease-modifying indications, which form the Compound–treats–Disease edges in Hetionet v1.0. Next, we identified types of paths (metapaths) that occurred more frequently between treatments than non-treatments (any compound–disease pair that is not a treatment). The advantage of this approach is that metapaths naturally correspond to mechanisms of pharmacological efficacy. For example, the Compound–binds–Gene–associates–Disease (CbGaD) metapath identifies when a drug binds to a protein corresponding to a gene involved in the disease.

We evaluated all 1,206 metapaths that traverse from compound to disease and have length of 2–4 (Figure 2A). To control for the different degrees of nodes, we used the degree-weighted path count (DWPC, see Methods) — which downweights paths going through highly-connected nodes [22] — to assess path prevalence. In addition, we compared the performance of each metapath to a baseline computed from permuted networks. Hetnet permutation preserves node degree while eliminating edge specificity, allowing us to isolate the portion of unpermuted metapath performance resulting from actual network paths. We refer to the permutation-adjusted performance measure as Δ AUROC. A positive Δ AUROC indicates that paths of the given type tended to occur more frequently between treatments than non-treatments, after accounting for different levels of connectivity (node degrees) in the hetnet. In general terms, Δ AUROC assesses whether paths of a given type were informative of drug efficacy.

-Figure 2: Performance by type and model coefficients. A) The performance of the DWPCs for 1,206 metapaths, organized by their composing metaedges. The larger dots represent metapaths that were significantly affected by permutation (false discovery rate < 5%). Metaedges are ordered by their best performing metapath. Since a metapath’s performance is limited by its least informative metaedge, the best performing metapath for a metaedge provides a lower bound on the pharmacologic utility of a given domain of information. B) Barplot of the model coefficients. Features were standardized prior to model fitting to make the coefficients comparable [39].
Figure 2: Performance by type and model coefficients. A) The performance of the DWPCs for 1,206 metapaths, organized by their composing metaedges. The larger dots represent metapaths that were significantly affected by permutation (false discovery rate < 5%). Metaedges are ordered by their best performing metapath. Since a metapath’s performance is limited by its least informative metaedge, the best performing metapath for a metaedge provides a lower bound on the pharmacologic utility of a given domain of information. B) Barplot of the model coefficients. Features were standardized prior to model fitting to make the coefficients comparable [39].
+Figure 2: Performance by type and model coefficients. A) The performance of the DWPCs for 1,206 metapaths, organized by their composing metaedges. The larger dots represent metapaths that were significantly affected by permutation (false discovery rate < 5%). Metaedges are ordered by their best performing metapath. Since a metapath’s performance is limited by its least informative metaedge, the best performing metapath for a metaedge provides a lower bound on the pharmacologic utility of a given domain of information. B) Barplot of the model coefficients. Features were standardized prior to model fitting to make the coefficients comparable [39].
Figure 2: Performance by type and model coefficients. A) The performance of the DWPCs for 1,206 metapaths, organized by their composing metaedges. The larger dots represent metapaths that were significantly affected by permutation (false discovery rate < 5%). Metaedges are ordered by their best performing metapath. Since a metapath’s performance is limited by its least informative metaedge, the best performing metapath for a metaedge provides a lower bound on the pharmacologic utility of a given domain of information. B) Barplot of the model coefficients. Features were standardized prior to model fitting to make the coefficients comparable [39].

Overall, 709 of the 1,206 metapaths exhibited a statistically significant Δ AUROC at a false discovery rate cutoff of 5%. These 709 metapaths included all 24 metaedges, suggesting that each type of relationship we integrated provided at least some therapeutic utility. However, not all metaedges were equally present in significant metapaths: 259 significant metapaths included a Compound–binds–Gene metaedge, whereas only 4 included a Gene–participates–Cellular Component metaedge. Table 3 lists the predictiveness of several metapaths of interest. Refer to the Discussion for our interpretation of these findings.

Table 2: Metaedges. Hetionet v1.0 contains 24 edge types (metaedges). For each metaedge, the table reports the abbreviation, the number of edges, the number of source nodes connected by the edges, and the number of target nodes connected by the edges. Note that all metaedges besides Gene→regulates→Gene are undirected. Table 2: Metaedges. Hetionet v1.0 contains 24 edge types (metaedges). For each metaedge, the table reports the abbreviation, the number of edges, the number of source nodes connected by the edges, and the number of target nodes connected by the edges. Note that all metaedges besides Gene→regulates→Gene are undirected.
Metaedge
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Predictions of drug efficacy

We implemented a machine learning approach to translate the network connectivity between a compound and a disease into a probability of treatment [40,41]. The approach relies on the 755 known treatments as positives and 29,044 non-treatments as negatives to train a logistic regression model. Note that 179,369 non-treatments were omitted as negative training observations because they had a prior probability of treatment equal to zero (see Methods). The features consisted of a prior probability of treatment, node degrees for 14 metaedges, and DWPCs for 123 metapaths that were well suited for modeling. A cross-validated elastic net was used to minimize overfitting, yielding a model with 31 features (Figure 2B). The DWPC features with negative coefficients appear to be included as node-degree-capturing covariates, i.e. they reflect the general connectivity of the compound and disease rather than specific paths between them. However, the 11 DWPC features with non-negligible positive coefficients represent the most salient types of connectivity for systematically modeling drug efficacy. See the metapaths with positive coefficients in Table 3 for unabbreviated names. As an example, the CcSEcCtD feature assesses whether the compound causes the same side effects as compounds that treat the disease. Alternatively, the CbGeAlD feature assesses whether the compound binds to genes that are expressed in the anatomies affected by the disease.

We applied this model to predict the probability of treatment between each of 1,538 connected compounds and each of 136 connected diseases, resulting in predictions for 209,168 compound–disease pairs [42], available at http://het.io/repurpose/. The 755 known disease-modifying indications were highly ranked (AUROC = 97.4%, Figure 3). The predictions also successfully prioritized two external validation sets: novel indications from DrugCentral (AUROC = 85.5%) and novel indications in clinical trial (AUROC = 70.0%). Together, these findings indicate that Project Rephetio has the ability to recognize efficacious compound–disease pairs.

-Figure 3: Predictions performance on four indication sets. We assess how well our predictions prioritize four sets of indications. A) The y-axis labels denote the number of indications (+) and non-indications (−) composing each set. Violin plots with quartile lines show the distribution of indications when compound–disease pairs are ordered by their prediction. In all four cases, the actual indications were ranked highly by our predictions. B) ROC Curves with AUROCs in the legend. C) Precision–Recall Curves with AUPRCs in the legend.
Figure 3: Predictions performance on four indication sets. We assess how well our predictions prioritize four sets of indications. A) The y-axis labels denote the number of indications (+) and non-indications (−) composing each set. Violin plots with quartile lines show the distribution of indications when compound–disease pairs are ordered by their prediction. In all four cases, the actual indications were ranked highly by our predictions. B) ROC Curves with AUROCs in the legend. C) Precision–Recall Curves with AUPRCs in the legend.
+Figure 3: Predictions performance on four indication sets. We assess how well our predictions prioritize four sets of indications. A) The y-axis labels denote the number of indications (+) and non-indications (−) composing each set. Violin plots with quartile lines show the distribution of indications when compound–disease pairs are ordered by their prediction. In all four cases, the actual indications were ranked highly by our predictions. B) ROC Curves with AUROCs in the legend. C) Precision–Recall Curves with AUPRCs in the legend.
Figure 3: Predictions performance on four indication sets. We assess how well our predictions prioritize four sets of indications. A) The y-axis labels denote the number of indications (+) and non-indications (−) composing each set. Violin plots with quartile lines show the distribution of indications when compound–disease pairs are ordered by their prediction. In all four cases, the actual indications were ranked highly by our predictions. B) ROC Curves with AUROCs in the legend. C) Precision–Recall Curves with AUPRCs in the legend.

Predictions were scaled to the overall prevalence of treatments (0.36%). Hence a compound–disease pair that received a prediction of 1% represents a 2-fold enrichment over the null probability. Of the 3,980 predictions with a probability exceeding 1%, 586 corresponded to known disease-modifying indications, leaving 3,394 repurposing candidates. For a given compound or disease, we provide the percentile rank of each prediction. Therefore, users can assess whether a given prediction is a top prediction for the compound or disease. In addition, our table-based prediction browser links to a custom guide for each prediction, which displays in the Neo4j Hetionet Browser. Each guide includes a query to display the top paths supporting the prediction and lists clinical trials investigating the indication.

Nicotine dependence case study

There are currently two FDA-approved medications for smoking cessation (varenicline and bupropion) that are not nicotine replacement therapies. PharmacotherapyDB v1.0 lists varenicline as a disease-modifying indication and nicotine itself as a symptomatic indication for nicotine dependence, but is missing bupropion. Bupropion was first approved for depression in 1985. Owing to the serendipitous observation that it decreased smoking in depressed patients taking this drug, Bupropion was approved for smoking cessation in 1997 [43]. Therefore we looked whether Project Rephetio could have predicted this repurposing. Bupropion was the 9th best prediction for nicotine dependence (99.5th percentile) with a probability 2.50-fold greater than the null. Figure 4 shows the top paths supporting the repurposing of bupropion.

-Figure 4: Evidence supporting the repurposing of bupropion for smoking cessation. This figure shows the 10 most supportive paths (out of 365 total) for treating nicotine dependence with bupropion, as available in this prediction’s Neo4j Browser guide. Our method detected that bupropion targets the CHRNA3 gene, which is also targeted by the known-treatment varenicline [44]. Furthermore, CHRNA3 is associated with nicotine dependence [45] and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChR) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia [46] as does varenicline [47], which could indicate an underlying common mechanism of action.
Figure 4: Evidence supporting the repurposing of bupropion for smoking cessation. This figure shows the 10 most supportive paths (out of 365 total) for treating nicotine dependence with bupropion, as available in this prediction’s Neo4j Browser guide. Our method detected that bupropion targets the CHRNA3 gene, which is also targeted by the known-treatment varenicline [44]. Furthermore, CHRNA3 is associated with nicotine dependence [45] and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChR) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia [46] as does varenicline [47], which could indicate an underlying common mechanism of action.
+Figure 4: Evidence supporting the repurposing of bupropion for smoking cessation. This figure shows the 10 most supportive paths (out of 365 total) for treating nicotine dependence with bupropion, as available in this prediction’s Neo4j Browser guide. Our method detected that bupropion targets the CHRNA3 gene, which is also targeted by the known-treatment varenicline [44]. Furthermore, CHRNA3 is associated with nicotine dependence [45] and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChR) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia [46] as does varenicline [47], which could indicate an underlying common mechanism of action.
Figure 4: Evidence supporting the repurposing of bupropion for smoking cessation. This figure shows the 10 most supportive paths (out of 365 total) for treating nicotine dependence with bupropion, as available in this prediction’s Neo4j Browser guide. Our method detected that bupropion targets the CHRNA3 gene, which is also targeted by the known-treatment varenicline [44]. Furthermore, CHRNA3 is associated with nicotine dependence [45] and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChR) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia [46] as does varenicline [47], which could indicate an underlying common mechanism of action.

Atop the nicotine dependence predictions were nicotine (10.97-fold over null), cytisine (10.58-fold), and galantamine (9.50-fold). Cytisine is widely used in Eastern Europe for smoking cessation due to its availability at a fraction of the cost of other pharmaceutical options [48]. In the last half decade, large scale clinical trials have confirmed cytisine’s efficacy [49,50]. Galantamine, an approved Alzheimer’s treatment, is currently in Phase 2 trial for smoking cessation and is showing promising results [51]. In summary, nicotine dependence illustrates Project Rephetio’s ability to predict efficacious treatments and prioritize historic and contemporary repurposing opportunities.

Epilepsy case study

Several factors make epilepsy an interesting disease for evaluating repurposing predictions [52]. Antiepileptic drugs work by increasing the seizure threshold — the amount of electric stimulation that is required to induce seizure. The effect of a drug on the seizure threshold can be cheaply and reliably tested in rodent models. As a result, the viability of most approved drugs in treating epilepsy is known.

We focused our evaluation on the top 100 scoring compounds — referred to as the epilepsy predictions in this section — after discarding a single combination drug. We classified each compound as anti-ictogenic (seizure suppressing), unknown (no established effect on the seizure threshold), or ictogenic (seizure generating) according to medical literature [52]. Of the top 100 epilepsy predictions, 77 were anti-ictogenic, 8 were unknown, and 15 were ictogenic (Figure 5A). Notably, the predictions contained 23 of the 25 disease-modifying antiepileptics in PharamcotherapyDB v1.0.

-Figure 5: Top 100 epilepsy predictions. A) Compounds — ranked from 1 to 100 by their predicted probability of treating epilepsy — are colored by their effect on seizures [52]. The highest predictions are almost exclusively anti-ictogenic. Further down the prediction list, the prevalence of drugs with an ictogenic (contraindication) or unknown (novel repurposing candidate) effect on epilepsy increases. All compounds shown received probabilities far exceeding the null probability of treatment (0.36%). B) A chemical similarity network of the epilepsy predictions, with each compound’s 2D structure [53]. Edges are Compound–resembles–Compound relationships from Hetionet v1.0. Nodes are colored by their effect on seizures. C) The relative contribution of important drug targets to each epilepsy prediction [53]. Specifically, pie charts show how the 8 most-supportive drug targets across all 100 epilepsy predictions contribute to individual predictions. Other Targets represents the aggregate contribution of all targets not listed. The network layout is identical to B.
Figure 5: Top 100 epilepsy predictions. A) Compounds — ranked from 1 to 100 by their predicted probability of treating epilepsy — are colored by their effect on seizures [52]. The highest predictions are almost exclusively anti-ictogenic. Further down the prediction list, the prevalence of drugs with an ictogenic (contraindication) or unknown (novel repurposing candidate) effect on epilepsy increases. All compounds shown received probabilities far exceeding the null probability of treatment (0.36%). B) A chemical similarity network of the epilepsy predictions, with each compound’s 2D structure [53]. Edges are Compound–resembles–Compound relationships from Hetionet v1.0. Nodes are colored by their effect on seizures. C) The relative contribution of important drug targets to each epilepsy prediction [53]. Specifically, pie charts show how the 8 most-supportive drug targets across all 100 epilepsy predictions contribute to individual predictions. Other Targets represents the aggregate contribution of all targets not listed. The network layout is identical to B.
+Figure 5: Top 100 epilepsy predictions. A) Compounds — ranked from 1 to 100 by their predicted probability of treating epilepsy — are colored by their effect on seizures [52]. The highest predictions are almost exclusively anti-ictogenic. Further down the prediction list, the prevalence of drugs with an ictogenic (contraindication) or unknown (novel repurposing candidate) effect on epilepsy increases. All compounds shown received probabilities far exceeding the null probability of treatment (0.36%). B) A chemical similarity network of the epilepsy predictions, with each compound’s 2D structure [53]. Edges are Compound–resembles–Compound relationships from Hetionet v1.0. Nodes are colored by their effect on seizures. C) The relative contribution of important drug targets to each epilepsy prediction [53]. Specifically, pie charts show how the 8 most-supportive drug targets across all 100 epilepsy predictions contribute to individual predictions. Other Targets represents the aggregate contribution of all targets not listed. The network layout is identical to B.
Figure 5: Top 100 epilepsy predictions. A) Compounds — ranked from 1 to 100 by their predicted probability of treating epilepsy — are colored by their effect on seizures [52]. The highest predictions are almost exclusively anti-ictogenic. Further down the prediction list, the prevalence of drugs with an ictogenic (contraindication) or unknown (novel repurposing candidate) effect on epilepsy increases. All compounds shown received probabilities far exceeding the null probability of treatment (0.36%). B) A chemical similarity network of the epilepsy predictions, with each compound’s 2D structure [53]. Edges are Compound–resembles–Compound relationships from Hetionet v1.0. Nodes are colored by their effect on seizures. C) The relative contribution of important drug targets to each epilepsy prediction [53]. Specifically, pie charts show how the 8 most-supportive drug targets across all 100 epilepsy predictions contribute to individual predictions. Other Targets represents the aggregate contribution of all targets not listed. The network layout is identical to B.

Many of the 77 anti-ictogenic compounds were not first-line antiepileptic drugs. Instead, they were used as ancillary drugs in the treatment of status epilepticus. For example, we predicted four halogenated ethers, two of which (isoflurane and desflurane) are used clinically to treat life-threatening seizures that persist despite treatment [54]. As inhaled anesthetics, these compounds are not appropriate as daily epilepsy medications, but are feasible for refractory status epilepticus where patients are intubated.

Given this high precision (77%), the 8 compounds of unknown effect are promising repurposing candidates. For example, acamprosate — whose top prediction was epilepsy — is a taurine analog that promotes alcohol abstinence. Support for this repurposing arose from acamprosate’s inhibition of the glutamate receptor and positive modulation of the GABAᴀ receptor (Figure 5C). If effective against epilepsy, acamprosate could serve a dual benefit for recovering alcoholics who experience seizures from alcohol withdrawal.

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Epilepsy case study

Next, we investigated which components of Hetionet contributed to the epilepsy predictions [52]. In total, 392,956 paths of 12 types supported the predictions. Using several different methods for grouping paths, we were able to quantify the aggregate biological evidence. Our algorithm primarily drew on two aspects of epilepsy: its known treatments (76% of the total support) and its genetic associations (22% of support). In contrast, our algorithm drew heavily on several aspects of the predicted compounds: their targeted genes (44%), their chemically similar compounds (30%), their pharmacologic classes, their palliative indications (5%), and their side effects (4%).

Specifically, 266,192 supporting paths originated with a Compound–binds–Gene relationship. Aggregating support by these genes shows the extent that 121 different drug targets contributed to the predictions [52]. In order of importance, the predictions targeted GABAᴀ receptors (15.3% of total support), cytochrome P450 enzymes (5.6%), the sodium channel (4.6%), glutamate receptors (3.8%), the calcium channel (2.7%), carbonic anhydrases (2.5%), cholinergic receptors (2.1%) and the potassium channel (1.4%). Besides cytochrome P450, which primarily influences pharmacokinetics [56], our method detected and leveraged bonafide anti-ictogenic mechanisms [57]. Figure 5C shows drug target contributions per compound and illustrates the considerable mechanistic diversity among the predictions.

Also notable are the 15 ictogenic compounds in our top 100 predictions. Nine of the ictogenic compounds share a tricyclic structure (Figure 5B), five of which are tricyclic antidepressants. While the ictogenic mechanisms of these antidepressants are still unclear [58], Figure 5C suggests their anticholinergic effects may be responsible [59], in accordance with previous theories [60].

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We also ranked the contribution of the 1,137 side effects that supported the epilepsy predictions through 117,720 CcSEcCtD paths. The top five side effects — ataxia (0.069% of total support), nystagmus (0.049%), diplopia (0.045%), somnolence (0.044%), and vomiting (0.043%) — reflect established adverse effects of antiepileptic drugs [61–65]. In summary, our method simultaneously identified the hallmark side effects of antiepileptic drugs while incorporating this knowledge to prioritize 1,538 compounds for anti-ictogenic activity.

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We also ranked the contribution of the 1,137 side effects that supported the epilepsy predictions through 117,720 CcSEcCtD paths. The top five side effects — ataxia (0.069% of total support), nystagmus (0.049%), diplopia (0.045%), somnolence (0.044%), and vomiting (0.043%) — reflect established adverse effects of antiepileptic drugs [61,62,63,64,65]. In summary, our method simultaneously identified the hallmark side effects of antiepileptic drugs while incorporating this knowledge to prioritize 1,538 compounds for anti-ictogenic activity.

Discussion

We created Hetionet v1.0 by integrating 29 resources into a single data structure — the hetnet. Consisting of 11 types of nodes and 24 types of relationships, Hetionet v1.0 brings more types of information together than previous leading-studies in biological data integration [66]. Moreover, we strove to create a reusable, extensible, and property-rich network. While all of the resources we include are publicly available, their integration was a time-intensive undertaking and required careful consideration of legal barriers to data reuse. Hetionet allows researchers to begin answering integrative questions without having to first spend months processing data.

Our public Neo4j instance allows users to immediately interact with Hetionet. Through the Cypher language, users can perform highly specialized graph queries with only a few lines of code. Queries can be executed in the web browser or programmatically from a language with a Neo4j driver. For users that are unfamiliar with Cypher, we include several example queries in a Browser guide. In contrast to traditional REST APIs, our public Neo4j instance provides users with maximal flexibility to construct custom queries by exposing the underlying database.

As data has grown more plentiful and diverse, so has the applicability of hetnets. Unfortunately, network science has been naturally fragmented by discipline resulting in relatively slow progress in integrating heterogeneous data. A 2014 analysis identified 78 studies using multilayer networks — a superset of hetnets (heterogeneous information networks) with the potential for additional dimensions, such as time. However, the studies relied on 26 different terms, 9 of which had multiple definitions [67,68]. Nonetheless, core infrastructure and algorithms for hetnets are emerging. Compared to the existing mathematical frameworks for multilayer networks that must deal with layers other than type (such as the aspect of time) [67], the primary obligation of hetnet algorithms is to be type aware. One goal of our project has been to unite hetnet research across disciplines. We approached this goal by making Project Rephetio entirely available online and inviting community feedback throughout the process [69].

Integrating every resource into a single interconnected data structure allowed us to assess systematic mechanisms of drug efficacy. Using the max performing metapath to assess the pharmacological utility of a metaedge (Figure 2A), we can divide our relationships into tiers of informativeness. The top tier consists of the types of information traditionally considered by pharmacology: Compound–treats–Disease, Pharmacologic Class–includes–Compound, Compound–resembles–Compound, Disease–resembles–Disease, and Compound–binds–Gene. The upper-middle tier consists of types of information that have been the focus of substantial medical study, but have only recently started to play a bigger role in drug development, namely the metaedges Disease–associates–Gene, Compound–causes–Side Effect, Disease–presents–Symptom, Disease–localizes–Anatomy, and Gene–interacts–Gene.

The lower-middle tier contains the transcriptomics metaedges such as Compound–downregulates–Gene, Anatomy–expresses–Gene, Gene→regulates→Gene, and Disease–downregulates–Gene. Much excitement surrounds these resources due to their high throughput and genome-wide scope, which offers a route to drug discovery that is less biased by existing knowledge. However, our findings suggest that these resources are only moderately informative of drug efficacy. Other lower-middle tier metaedges were the product of time-intensive biological experimentation, such as Gene–participates–Pathway, Gene–participates–Molecular Function, and Gene–participates–Biological Process. Unlike the top tier resources, this knowledge has historically been pursued for basic science rather than primarily medical applications. The weak yet appreciable performance of the Gene–covaries–Gene suggests the synergy between the fields of evolutionary genomics and disease biology. The lower tier included the Gene–participates–Cellular Component metaedge, which may reflect that the relevance of cellular location to pharmacology is highly case dependent and not amenable to systematic profiling.

-

The performance of specific metapaths (Table 3) provides further insight. For example, significant emphasis has been put on the use of transcriptional data for drug repurposing [30]. One common approach has been to identify compounds with opposing transcriptional signatures to a disease [18,70]. However, several systematic studies report underwhelming performance of this approach [24–26] — a finding supported by the low performance of the CuGdD and CdGuD metapaths in Project Rephetio. Nonetheless, other transcription-based methods showed some promise. Compounds with similar transcriptional signatures were prone to treating the same disease (CuGuCtD and CdGdCtD metapaths), while compounds with opposing transcriptional signatures were slightly averse to treating the same disease (CuGdCtD and CdGuCtD metapaths). In contrast, diseases with similar transcriptional profiles were not prone to treatment by the same compound (CtDdGuD and CtDuGdD).

+

The performance of specific metapaths (Table 3) provides further insight. For example, significant emphasis has been put on the use of transcriptional data for drug repurposing [30]. One common approach has been to identify compounds with opposing transcriptional signatures to a disease [18,70]. However, several systematic studies report underwhelming performance of this approach [24,25,26] — a finding supported by the low performance of the CuGdD and CdGuD metapaths in Project Rephetio. Nonetheless, other transcription-based methods showed some promise. Compounds with similar transcriptional signatures were prone to treating the same disease (CuGuCtD and CdGdCtD metapaths), while compounds with opposing transcriptional signatures were slightly averse to treating the same disease (CuGdCtD and CdGuCtD metapaths). In contrast, diseases with similar transcriptional profiles were not prone to treatment by the same compound (CtDdGuD and CtDuGdD).

By comparably assessing the informativeness of different metaedges and metapaths, Project Rephetio aims to guide future research towards promising data types and analyses. One caveat is that omics-scale experimental data will likely play a larger role in developing the next generation of pharmacotherapies. Hence, were performance reevaluated on treatments discovered in the forthcoming decades, the predictive ability of these data types may rise. Encouragingly, most data types were at least weakly informative and hence suitable for further study. Ideally, different data types would provide orthogonal information. However, our model for whether a compound treats a disease focused on 11 metapaths — a small portion of the hundreds of metapaths available. While parsimony aids interpretation, our model did not draw on the weakly-predictive high-throughput data types — which are intriguing for their novelty, scalability, and cost-effectiveness — as much as we had hypothesized.

Instead our model selected types of information traditionally considered in pharmacology. However unlike a pharmacologist whose area of expertise may be limited to a few drug classes, our model was able to predict probabilities of treatment for all 209,168 compound–disease pairs. Furthermore, our model systematically learned the importance of each type of network connectivity. For any compound–disease pair, we now can immediately provide the top network paths supporting its therapeutic efficacy. A traditional pharmacologist may be able to produce a similar explanation, but likely not until spending substantial time researching the compound’s pharmacology, the disease’s pathophysiology, and the molecular relationships in between. Accordingly, we hope certain predictions will spur further research, such as trials to investigate the off-label use of acamprosate for epilepsy, which is supported by one animal model [71].

As demonstrated by the 15 ictogenic compounds in our top 100 epilepsy predictions, Project Rephetio’s predictions can include contraindications in addition to indications. Since many of Hetionet v1.0’s relationship types are general (e.g. the Compound–binds–Gene relationship type conflates antagonist with agonist effects), we expect some high scoring predictions to exacerbate rather than treat the disease. However, the predictions made by Hetionet v1.0 represent such substantial relative enrichment over the null that uncovering the correct directionality is a logical next step and worth undertaking. Going forward, advances in automated mining of the scientific literature could enable extraction of precise relationship types at omics scale [72,73].

@@ -635,21 +635,21 @@

Methods

Nodes

Nodes encode entities. We extracted nodes from standard terminologies, which provide curated vocabularies to enable data integration and prevent concept duplication. The ease of mapping external vocabularies, adoption, and comprehensiveness were primary selection criteria. Hetionet v1.0 includes nodes from 5 ontologies — which provide hierarchy of entities for a specific domain — selected for their conformity to current best practices [76].

We selected 137 terms from the Disease Ontology [77,78] (which we refer to as DO Slim [79,80]) as our disease set. Our goal was to identify complex diseases that are distinct and specific enough to be clinically relevant yet general enough to be well annotated. To this end, we included diseases that have been studied by GWAS and cancer types from TopNodes_DOcancerslim [81]. We ensured that no DO Slim disease was a subtype of another DO Slim disease. Symptoms were extracted from MeSH by taking the 438 descendants of Signs and Symptoms [82,83].

-

Approved small molecule compounds with documented chemical structures were extracted from DrugBank version 4.2 [84–86]. Unapproved compounds were excluded because our focus was repurposing. In addition, unapproved compounds tend to be less studied than approved compounds making them less attractive for our approach where robust network connectivity is critical. Finally, restricting to small molecules with known documented structures enabled us to map between compound vocabularies (see Mappings).

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Side effects were extracted from SIDER version 4.1 [87–89]. SIDER codes side effects using UMLS identifiers [90], which we also adopted. Pharmacologic Classes were extracted from the DrugCentral data repository [91,92]. Only pharmacologic classes corresponding to the “Chemical/Ingredient”, “Mechanism of Action”, and “Physiologic Effect” FDA class types were included to avoid pharmacologic classes that were synonymous with indications [92].

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Protein-coding human genes were extracted from Entrez Gene [93–95]. Anatomical structures, which we refer to as anatomies, were extracted from Uberon [96]. We selected a subset of 402 Uberon terms by excluding terms known not to exist in humans and terms that were overly broad or arcane [97,98].

-

Pathways were extracted by combining human pathways from WikiPathways [99,100], Reactome [101], and the Pathway Interaction Database [102]. The latter two resources were retrieved from Pathway Commons (RRID:SCR_002103) [103], which compiles pathways from several providers. Duplicate pathways and pathways without multiple participating genes were removed [104,105]. Biological processes, cellular components, and molecular functions were extracted from the Gene Ontology [106]. Only terms with 2–1000 annotated genes were included.

+

Approved small molecule compounds with documented chemical structures were extracted from DrugBank version 4.2 [84,85,86]. Unapproved compounds were excluded because our focus was repurposing. In addition, unapproved compounds tend to be less studied than approved compounds making them less attractive for our approach where robust network connectivity is critical. Finally, restricting to small molecules with known documented structures enabled us to map between compound vocabularies (see Mappings).

+

Side effects were extracted from SIDER version 4.1 [87,88,89]. SIDER codes side effects using UMLS identifiers [90], which we also adopted. Pharmacologic Classes were extracted from the DrugCentral data repository [91,92]. Only pharmacologic classes corresponding to the “Chemical/Ingredient”, “Mechanism of Action”, and “Physiologic Effect” FDA class types were included to avoid pharmacologic classes that were synonymous with indications [92].

+

Protein-coding human genes were extracted from Entrez Gene [93,94,95]. Anatomical structures, which we refer to as anatomies, were extracted from Uberon [96]. We selected a subset of 402 Uberon terms by excluding terms known not to exist in humans and terms that were overly broad or arcane [97,98].

+

Pathways were extracted by combining human pathways from WikiPathways [100,99], Reactome [101], and the Pathway Interaction Database [102]. The latter two resources were retrieved from Pathway Commons (RRID:SCR_002103) [103], which compiles pathways from several providers. Duplicate pathways and pathways without multiple participating genes were removed [104,105]. Biological processes, cellular components, and molecular functions were extracted from the Gene Ontology [106]. Only terms with 2–1000 annotated genes were included.

Mappings

Before adding relationships, all identifiers needed to be converted into the vocabularies matching that of our nodes. Oftentimes, our node vocabularies included external mappings. For example, the Disease Ontology includes mappings to MeSH, UMLS, and the ICD, several of which we submitted during the course of this study [107]. In a few cases, the only option was to map using gene symbols, a disfavored method given that it can lead to ambiguities.

When mapping external disease concepts onto DO Slim, we used transitive closure. For example, the UMLS concept for primary progressive multiple sclerosis (C0751964) was mapped to the DO Slim term for multiple sclerosis (DOID:2377).

Chemical vocabularies presented the greatest mapping challenge [85], since these are poorly standardized [108]. UniChem’s [109] Connectivity Search [110] was used to map compounds, which maps by atomic connectivity (based on First InChIKey Hash Blocks [111]) and ignores small molecular differences.

Edges

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Anatomy–downregulates–Gene and Anatomy–upregulates–Gene edges [112–114] were extracted from Bgee [115], which computes differentially expressed genes by anatomy in post-juvenile adult humans. Anatomy–expresses–Gene edges were extracted from Bgee and TISSUES [116–118].

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Compound–binds–Gene edges were aggregated from BindingDB [119,120], DrugBank [84,121], and DrugCentral [91]. Only binding relationships to single proteins with affinities of at least 1 μM (as determined by Kd, Kᵢ, or IC₅₀) were selected from the October 2015 release of BindingDB [122,123]. Target, carrier, transporter, and enzyme interactions with single proteins (i.e. excluding protein groups) were extracted from DrugBank 4.2 [86,124]. In addition, all mapping DrugCentral target relationships were included [92].

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Compound–treats–Disease (disease-modifying indications) and Compound–palliates–Disease (symptomatic indications) edges are from PharmacotherapyDB as described in Intermediate resources. Compound–causes–Side Effect edges were obtained from SIDER 4.1 [87–89], which uses natural language processing to identify side effects in drug labels. Compound–resembles–Compound relationships [86,125,126] represent chemical similarity and correspond to a Dice coefficient ≥ 0.5 [127] between extended connectivity fingerprints [128,129]. Pharmacologic Class–includes–Compound edges were extracted from DrugCentral for three FDA class types [91,92]. Compound–downregulates–Gene and Compound–upregulates–Gene relationships were computed from LINCS L1000 as described in Intermediate resources.

+

Anatomy–downregulates–Gene and Anatomy–upregulates–Gene edges [112,113,114] were extracted from Bgee [115], which computes differentially expressed genes by anatomy in post-juvenile adult humans. Anatomy–expresses–Gene edges were extracted from Bgee and TISSUES [116,117,118].

+

Compound–binds–Gene edges were aggregated from BindingDB [119,120], DrugBank [121,84], and DrugCentral [91]. Only binding relationships to single proteins with affinities of at least 1 μM (as determined by Kd, Kᵢ, or IC₅₀) were selected from the October 2015 release of BindingDB [122,123]. Target, carrier, transporter, and enzyme interactions with single proteins (i.e. excluding protein groups) were extracted from DrugBank 4.2 [124,86]. In addition, all mapping DrugCentral target relationships were included [92].

+

Compound–treats–Disease (disease-modifying indications) and Compound–palliates–Disease (symptomatic indications) edges are from PharmacotherapyDB as described in Intermediate resources. Compound–causes–Side Effect edges were obtained from SIDER 4.1 [87,88,89], which uses natural language processing to identify side effects in drug labels. Compound–resembles–Compound relationships [125,126,86] represent chemical similarity and correspond to a Dice coefficient ≥ 0.5 [127] between extended connectivity fingerprints [128,129]. Pharmacologic Class–includes–Compound edges were extracted from DrugCentral for three FDA class types [91,92]. Compound–downregulates–Gene and Compound–upregulates–Gene relationships were computed from LINCS L1000 as described in Intermediate resources.

Disease–associates–Gene edges were extracted from the GWAS Catalog [130], DISEASES [131,132], DisGeNET [133,134], and DOAF [135,136]. The GWAS Catalog compiles disease–SNP associations from published GWAS [137]. We aggregated overlapping loci associated with each disease and identified the mode reported gene for each high confidence locus [138,139]. DISEASES integrates evidence of association from text mining, curated catalogs, and experimental data [140]. Associations from DISEASES with integrated scores ≥ 2 were included after removing the contribution of DistiLD. DisGeNET integrates evidence from over 10 sources and reports a single score for each association [141,142]. Associations with scores ≥ 0.06 were included. DOAF mines Entrez Gene GeneRIFs (textual annotations of gene function) for disease mentions [143]. Associations with 3 or more supporting GeneRIFs were included. Disease–downregulates–Gene and Disease–upregulates–Gene relationships [144,145] were computed using STARGEO as described in Intermediate resources.

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Disease–localizes–Anatomy, Disease–presents–Symptom, and Disease–resembles–Disease edges were calculated from MEDLINE co-occurrence [82,146]. MEDLINE is a subset of 21 million PubMed articles for which designated human curators have assigned topics. When retrieving articles for a given topic (MeSH term), we activated two non-default search options as specified below: majr for selecting only articles where the topic is major and noexp for suppressing explosion (returning articles linked to MeSH subterms). We identified 4,161,769 articles with two or more disease topics; 696,252 articles with both a disease topic (majr) and an anatomy topic (noexp) [147]; and 363,928 articles with both a disease topic (majr) and a symptom topic (noexp). We used a Fisher’s exact test [148] to identify pairs of terms that occurred together more than would be expected by chance in their respective corpus. We included co-occurring terms with p < 0.005 in Hetionet v1.0.

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Gene→regulates→Gene directed edges were generated from the LINCS L1000 genetic interference screens (see Intermediate resources) and indicate that knockdown or overexpression of the source gene significantly dysregulated the target gene [149,150]. Gene–covaries–Gene edges represent evolutionary rate covariation ≥ 0.75 [151–153]. Gene–interacts–Gene edges [154,155] represent when two genes produce physically-interacting proteins. We compiled these interactions from the Human Interactome Database [156–159], the Incomplete Interactome [160], and our previous study [22]. Gene–participates–Biological Process, Gene–participates–Cellular Component, and Gene–participates–Molecular Function edges are from Gene Ontology annotations [161]. As described in Intermediate resources, annotations were propagated [162,163]. Gene–participates–Pathway edges were included from the human pathway resources described in the Nodes section [104,105].

+

Disease–localizes–Anatomy, Disease–presents–Symptom, and Disease–resembles–Disease edges were calculated from MEDLINE co-occurrence [146,82]. MEDLINE is a subset of 21 million PubMed articles for which designated human curators have assigned topics. When retrieving articles for a given topic (MeSH term), we activated two non-default search options as specified below: majr for selecting only articles where the topic is major and noexp for suppressing explosion (returning articles linked to MeSH subterms). We identified 4,161,769 articles with two or more disease topics; 696,252 articles with both a disease topic (majr) and an anatomy topic (noexp) [147]; and 363,928 articles with both a disease topic (majr) and a symptom topic (noexp). We used a Fisher’s exact test [148] to identify pairs of terms that occurred together more than would be expected by chance in their respective corpus. We included co-occurring terms with p < 0.005 in Hetionet v1.0.

+

Gene→regulates→Gene directed edges were generated from the LINCS L1000 genetic interference screens (see Intermediate resources) and indicate that knockdown or overexpression of the source gene significantly dysregulated the target gene [149,150]. Gene–covaries–Gene edges represent evolutionary rate covariation ≥ 0.75 [151,152,153]. Gene–interacts–Gene edges [154,155] represent when two genes produce physically-interacting proteins. We compiled these interactions from the Human Interactome Database [156,157,158,159], the Incomplete Interactome [160], and our previous study [22]. Gene–participates–Biological Process, Gene–participates–Cellular Component, and Gene–participates–Molecular Function edges are from Gene Ontology annotations [161]. As described in Intermediate resources, annotations were propagated [162,163]. Gene–participates–Pathway edges were included from the human pathway resources described in the Nodes section [104,105].

Directionality

Whether a certain type of relationship has directionality is defined at the metaedge level. Directed metaedges are only necessary when they connect a metanode to itself and correspond to an asymmetric relationship. In the case of Hetionet v1.0, the sole directed metaedge was Gene→regulates→Gene. To demonstrate the implications of directionality, Hetionet v1.0 contains two relationships between the genes HADH and STAT1: HADH–interacts–STAT1 and HADH→regulates→STAT1. Both edges can be represented in the inverse orientation: STAT1–interacts–HADH and STAT1←regulates←HADH. However due to directed nature of the regulates relationship, STAT1→regulates→HADH is a distinct edge, which does not exist in the network. Similarly, HADH–associates–obesity and obesity–associates–HADH are inverse orientations of the same underlying undirected relationship. Accordingly, the following path exists in the network: obesity–associates–HADH→regulates→STAT1, which can also be inverted to STAT1←regulates←HADH–associates–obesity.

Intermediate resources

@@ -664,24 +664,24 @@

Transcripti

The consensus signatures for genetic perturbations allowed us to assess various characteristics of the L1000 dataset. First, we looked at whether genetic interference dysregulated its target gene in the expected direction [169]. Looking at measured z-scores for target genes, we found that the knockdown perturbations were highly reliable, while the overexpression perturbations were only moderately reliable with 36% of overexpression perturbations downregulating their target. However, imputed z-scores for target genes barely exceeded chance at responding in the expected direction to interference. Hence, we concluded that the imputation quality of LINCS L1000 is poor. However, when restricting to significantly dyseregulated targets, 22 out of 29 imputed genes responded in the expected direction. This provides some evidence that the directional fidelity of imputation is higher for significantly dysregulated genes. Finally, we found that the transcriptional signatures of knocking down and overexpressing the same gene were positively correlated 65% of the time, suggesting the presence of a general stress response [170].

Based on these findings, we performed additional filtering of signifcantly dysregulated genes when building Hetionet v1.0. Compound–down/up-regulates–Gene relationships were restricted to the 125 most significant per compound-direction-status combination (status refers to measured versus imputed). For genetic interference perturbations, we restricted to the 50 most significant genes per gene-direction-status combination and merged the remaining edges into a single Gene→regulates→Gene relationship type containing both knockdown and overexpression perturbations.

PharmacotherapyDB: physician curated indications

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We created PharmacotherapyDB, an open catalog of drug therapies for disease [171–173]. Version 1.0 contains 755 disease-modifying therapies and 390 symptomatic therapies between 97 diseases and 601 compounds.

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This resource was motivated by the need for a gold standard of medical indications to train and evaluate our approach. Initially, we identified four existing indication catalogs [174]: MEDI-HPS which mined indications from RxNorm, SIDER 2, MedlinePlus, and Wikipedia [175]; LabeledIn which extracted indications from drug labels via human curation [176–178]; EHRLink which identified medication–problem pairs that clinicians linked together in electronic health records [179,180]; and indications from PREDICT, which were compiled from UMLS relationships, drugs.com, and drug labels [24]. After mapping to DO Slim and DrugBank Slim, the four resources contained 1,388 distinct indications.

+

We created PharmacotherapyDB, an open catalog of drug therapies for disease [171,172,173]. Version 1.0 contains 755 disease-modifying therapies and 390 symptomatic therapies between 97 diseases and 601 compounds.

+

This resource was motivated by the need for a gold standard of medical indications to train and evaluate our approach. Initially, we identified four existing indication catalogs [174]: MEDI-HPS which mined indications from RxNorm, SIDER 2, MedlinePlus, and Wikipedia [175]; LabeledIn which extracted indications from drug labels via human curation [176,177,178]; EHRLink which identified medication–problem pairs that clinicians linked together in electronic health records [179,180]; and indications from PREDICT, which were compiled from UMLS relationships, drugs.com, and drug labels [24]. After mapping to DO Slim and DrugBank Slim, the four resources contained 1,388 distinct indications.

However, we noticed that many indications were palliative and hence problematic as a gold standard of pharmacotherapy for our in silico approach. Therefore, we recruited two practicing physicians to curate the 1,388 preliminary indications [181]. After a pilot on 50 indications, we defined three classifications: disease modifying meaning a drug that therapeutically changes the underlying or downstream biology of the disease; symptomatic meaning a drug that treats a significant symptom of the disease; and non-indication meaning a drug that neither therapeutically changes the underlying or downstream biology nor treats a significant symptom of the disease. Both curators independently classified all 1,388 indications.

The two curators disagreed on 444 calls (Cohen’s κ = 49.9%). We then recruited a third practicing physician, who reviewed all 1,388 calls and created a detailed explanation of his methodology [181]. We proceeded with the third curator’s calls as the consensus curation. The first two curators did have reservations with classifying steroids as disease modifying for autoimmune diseases. We ultimately considered that these indications met our definition of disease modifying, which is based on a pathophysiological rather than clinical standard. Accordingly, therapies we consider disease modifying may not be used to alter long-term disease course in the modern clinic due to a poor risk–benefit ratio.

User-friendly Gene Ontology annotations

We created a browser (http://git.dhimmel.com/gene-ontology/) to provide straightforward access to Gene Ontology annotations [162,163]. Our service provides annotations between Gene Ontology terms and Entrez Genes. The user chooses propagated/direct annotation and all/experimental evidence. Annotations are currently available for 37 species and downloadable as user-friendly TSV files.

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We committed to openly releasing our data and analyses from the origin of the project [182]. Our goals were to contribute to the advancement of science [183,184], maximize our impact [185,186], and enable reproducibility [187–189]. These objectives required publicly distributing and openly licensing Hetionet and Project Rephetio data and analyses [190,191].

+

We committed to openly releasing our data and analyses from the origin of the project [182]. Our goals were to contribute to the advancement of science [183,184], maximize our impact [185,186], and enable reproducibility [187,188,189]. These objectives required publicly distributing and openly licensing Hetionet and Project Rephetio data and analyses [190,191].

Since we integrated only public resources, which were overwhelmingly funded by academic grants, we had assumed that our project and open sharing of our network would not be an issue. However, upon releasing a preliminary version of Hetionet [192], a community reviewer informed us of legal barriers to integrating public data. In essence, both copyright (rights of exclusivity automatically granted to original works) and terms of use (rules that users must agree to in order to use a resource) place legally-binding restrictions on data reuse. In short, public data is not by default open data.

Hetionet v1.0 integrates 29 resources (Table 4), but two resources were removed prior to the v1.0 release. Of the total 31 resources [193], five were United States government works not subject to copyright, and twelve had licenses that met the Open Definition of knowledge version 2.1. Four resources allowed only non-commercial reuse. Most problematic were the remaining nine resources that had no license — which equates to all rights reserved by default and forbids reuse [194] — and one resource that explicitly forbid redistribution.

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Table 3: The predictiveness of select metapaths. A small selection of interesting or influential metapaths is provided (complete table online). Len. refers to number of metaedges composing the metapath. Δ AUROC and −log10(p) assess the performance of a metapath’s DWPC in discriminating treatments from non-treatments (in the all-features stage as described in Methods). p assesses whether permutation affected AUROC. For reference, p = 0.05 corresponds to −log10(p) = 1.30. Note that several metapaths shown here provided little evidence that Δ AUROC ≠ 0 underscoring their poor ability to predict whether a compound treated a disease. Coef. reports a metapath’s logistic regression coefficient as seen in Figure 2B. Metapaths removed in feature selection have missing coefficients whereas metapaths given zero-weight by the elastic net have coef. = 0.0. Table 3: The predictiveness of select metapaths. A small selection of interesting or influential metapaths is provided (complete table online). Len. refers to number of metaedges composing the metapath. Δ AUROC and −log10(p) assess the performance of a metapath’s DWPC in discriminating treatments from non-treatments (in the all-features stage as described in Methods). p assesses whether permutation affected AUROC. For reference, p = 0.05 corresponds to −log10(p) = 1.30. Note that several metapaths shown here provided little evidence that Δ AUROC ≠ 0 underscoring their poor ability to predict whether a compound treated a disease. Coef. reports a metapath’s logistic regression coefficient as seen in Figure 2B. Metapaths removed in feature selection have missing coefficients whereas metapaths given zero-weight by the elastic net have coef. = 0.0.
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Table 4: The 29 public data resources integrated to construct Hetionet v1.0. Components notes which types of nodes and edges in Hetionet v1.0 derived from the resource (as per the abbreviations in Tables 1 & 2). Cat. notes the general category of license [193]. Category 1 refers to United States government works that we deemed were not subject to copyright. Category 2 refers to resources with licenses that allow use, redistribution, and modification (although some restrictions may still exist). The subset of category 2 licenses that we deemed to meet the the Open Definition are denoted with ᴼᴰ. Category 4 refers to resources without a license, hence with all rights reserved. References provides Research Resource Identifiers as well as citations to resource publications and related Project Rephetio materials. For information on license provenance, institutional affiliations, and funding for each resource, see the online table.
+-+ @@ -699,21 +699,21 @@ - + - + - + @@ -734,7 +734,7 @@ - + @@ -755,21 +755,21 @@ - + - + - + @@ -783,21 +783,21 @@ - + - + - + @@ -818,7 +818,7 @@ - + @@ -832,21 +832,21 @@ - + - + - + @@ -867,14 +867,14 @@ - + - + @@ -888,7 +888,7 @@ - + @@ -899,13 +899,13 @@
Table 4: The 29 public data resources integrated to construct Hetionet v1.0. Components notes which types of nodes and edges in Hetionet v1.0 derived from the resource (as per the abbreviations in Tables 1 & 2). Cat. notes the general category of license [193]. Category 1 refers to United States government works that we deemed were not subject to copyright. Category 2 refers to resources with licenses that allow use, redistribution, and modification (although some restrictions may still exist). The subset of category 2 licenses that we deemed to meet the the Open Definition are denoted with ᴼᴰ. Category 4 refers to resources without a license, hence with all rights reserved. References provides Research Resource Identifiers as well as citations to resource publications and related Project Rephetio materials. For information on license provenance, institutional affiliations, and funding for each resource, see the online table.
G custom 1RRID:SCR_002473 [93–95]RRID:SCR_002473 [93,94,95]
LabeledIn CtD, CpD custom 1RRID:SCR_015667 [176–178]RRID:SCR_015667 [176,177,178]
MEDLINE DlA, DpS, DrD custom 1RRID:SCR_002185 [82,146]RRID:SCR_002185 [146,82]
MeSH D CC BY 3.0 2ᴼᴰRRID:SCR_000476 [77–80]RRID:SCR_000476 [77,78,79,80]
DISEASES BP, CC, MF, GpBP, GpCC, GpMF CC BY 4.0 2ᴼᴰRRID:SCR_002811 [106,161–163]RRID:SCR_002811 [106,161,162,163]
GWAS Catalog DaG custom 2ᴼᴰRRID:SCR_012745 [130,137–139]RRID:SCR_012745 [130,137,138,139]
Reactome PW, GpPW custom 2ᴼᴰRRID:SCR_003485 [101,103–105]RRID:SCR_003485 [101,103,104,105]
LINCS L1000 AeG CC BY 4.0 2ᴼᴰRRID:SCR_015665 [116–118]RRID:SCR_015665 [116,117,118]
Uberon A CC BY 3.0 2ᴼᴰRRID:SCR_010668 [96–98]RRID:SCR_010668 [96,97,98]
WikiPathways PW, GpPW CC BY 3.0 / custom 2ᴼᴰRRID:SCR_002134 [99,100,104,105]RRID:SCR_002134 [100,104,105,99]
BindingDB C, CbG, CrC custom 2RRID:SCR_002700 [84–86,196]RRID:SCR_002700 [196,84,85,86]
MEDI CtD, CpD CC BY-NC-SA 3.0 2[24,174][174,24]
SIDER SE, CcSE CC BY-NC-SA 4.0 2RRID:SCR_004321 [87–89]RRID:SCR_004321 [87,88,89]
Bgee AeG, AdG, AuG 4RRID:SCR_002028 [112–115]RRID:SCR_002028 [112,113,114,115]
DOAF GcG 4RRID:SCR_015669 [151–153]RRID:SCR_015669 [151,152,153]
hetio-dag GiG 4[22,154,155][154,155,22]
Incomplete Interactome GiG 4RRID:SCR_015670 [154–159]RRID:SCR_015670 [154,155,156,157,158,159]
STARGEO
-

Additional difficulty resulted from license incompatibles across resources, which was caused primarily by non-commercial and share-alike stipulations. Furthermore, it was often unclear who owned the data [198]. Therefore, we sought input from legal experts and chronicled our progress [193,195–197,199].

+

Additional difficulty resulted from license incompatibles across resources, which was caused primarily by non-commercial and share-alike stipulations. Furthermore, it was often unclear who owned the data [198]. Therefore, we sought input from legal experts and chronicled our progress [193,195,196,197,199].

Ultimately, we did not find an ideal solution. We had to choose between absolute compliance and Hetionet: strictly adhering to copyright and licensing arrangements would have decimated the network. On the other hand, in the United States, mere facts are not subject to copyright, and fair use doctrine helps protect reuse that is transformative and educational. Hence, we choose a path forward which balanced legal, normative, ethical, and scientific considerations.

If a resource was in the public domain, we licensed any derivatives as CC0 1.0. For resources licensed to allow reuse, redistribution, and modification, we transmitted their licenses as properties on the specific nodes and relationships in Hetionet v1.0. For all other resources — for example, resources without licenses or with licenses that forbid redistribution — we sent permission requests to their creators. The median time till first response to our permission requests was 16 days, with only 2 resources affirmatively granting us permission. We did not receive any responses asking us to remove a resource. However, we did voluntarily remove MSigDB [200], since its license was highly problematic [199]. As a result of our experience, we recommend that publicly-funded data should be explicitly dedicated to the public domain whenever possible.

Permuted hetnets

From Hetionet, we derived five permuted hetnets [201]. The permutations preserve node degree but eliminate edge specificity by employing an algorithm called XSwap to randomly swap edges [202]. To extend XSwap to hetnets [22], we permuted each metaedge separately, so that edges were only swapped with other edges of the same type. We adopted a Markov chain approach, whereby the first permuted hetnet was generated from Hetionet v1.0, the second permuted hetnet was generated from the first, and so on. For each metaedge, we assessed the percent of edges unchanged as the algorithm progressed to ensure that a sufficient number of swaps had been performed to randomize the network [201]. Permuted hetnets are useful for computing the baseline performance of meaningless edges while preserving node degree [203]. Since, our use of permutation focused on assessing Δ AUROC, a small number of permuted hetnets was sufficient, as the variability in a metapath’s AUROC across the permuted hetnets was low.

Graph databases & Neo4j

-

Traditional relational databases — such as SQLite, MySQL, and PostgreSQL — excel at storing highly structured data in tables. Connectivity between tables is accomplished using foreign-key references between columns. However, for many biomedical applications the connectivity between entities is of foremost importance. Furthermore, enforcing a rigid structure of what attributes an entity may possess is less important and often unnecessarily prohibitive. Graph databases focus instead on capturing connectivity (relationships) between entities (nodes). Accordingly, graph databases such as Neo4j offer greater ease when modeling biomedical relationships and superior performance when traversing many levels of connectivity [204,205]. Until recently, graph database adoption in bioinformatics was limited [206]. However lately, the demand to model and capture biological connectivity at scale has led to increasing adoption [207–210].

+

Traditional relational databases — such as SQLite, MySQL, and PostgreSQL — excel at storing highly structured data in tables. Connectivity between tables is accomplished using foreign-key references between columns. However, for many biomedical applications the connectivity between entities is of foremost importance. Furthermore, enforcing a rigid structure of what attributes an entity may possess is less important and often unnecessarily prohibitive. Graph databases focus instead on capturing connectivity (relationships) between entities (nodes). Accordingly, graph databases such as Neo4j offer greater ease when modeling biomedical relationships and superior performance when traversing many levels of connectivity [204,205]. Until recently, graph database adoption in bioinformatics was limited [206]. However lately, the demand to model and capture biological connectivity at scale has led to increasing adoption [207,208,209,210].

We used the Neo4j graph database for storing and operating on Hetionet and noticed major benefits from tapping into this large open source ecosystem [211]. Persistent storage with immediate access and the Cypher query language — a sort of SQL for hetnets — were two of the biggest benefits. To facilitate our migration to Neo4j, we updated hetio — our existing Python package for hetnets [212] — to export networks into Neo4j and DWPC queries to Cypher. In addition, we created an interactive GraphGist for Project Rephetio, which introduces our approach and showcases its Cypher queries. Finally, we created a public Neo4j instance [213], which leverages several modern technologies such Neo4j Browser guides, cloud hosting with HTTPS, and Docker deployment [214,215].

Machine learning approach

Project Rephetio relied on the previously-published DWPC metric to generate features for compound–disease pairs. The DWPC measures the prevalence of a given metapath between a given source and target node [22]. It is calculated by first extracting all paths from the source to target node that follow the specified metapath. Next, each path is weighted by taking the product of the node degrees along the path raised to a negative exponent. This damping exponent — the sole parameter — thereby determines the extent that paths through high-degree nodes are downweighted: we chose w = 0.4 based on our past optimizations [22]. The DWPC equals the sum of the path weights (referred to as path-degree products). Traversing the hetnet to extract all paths between a source and target node, which we performed in Neo4j, is the most computationally intensive step in computing DWPCs [216]. For future work, we are exploring matrix multiplication approaches, which could improve runtime several orders of magnitude.

@@ -925,12 +925,12 @@

Indication sets

  • Clinical Trial — We compiled indications that have been investigated by clinical trial from ClinicalTrials.gov [225]. This set contains 5,594 indications. Since these indications were not manually curated and clinical trials often show a lack of efficacy, we expected lower performance on this set.
  • Symptomatic — 390 symptomatic indications from PharacotherapyDB. These edges are included in the hetnet as palliates edges.
    • -

    Only the Clinical Trial and DrugCentral indication sets were used for external validation, since the Disease Modifying and Symptomatic indications were included in the hetnet. As an aside, several additional indication catalogs have recently been published, which future studies may want to also consider [174,226–228].

    +

    Only the Clinical Trial and DrugCentral indication sets were used for external validation, since the Disease Modifying and Symptomatic indications were included in the hetnet. As an aside, several additional indication catalogs have recently been published, which future studies may want to also consider [174,226,227,228].

    Realtime open science & Thinklab

    We conducted our study using Thinklab — a platform for realtime open collaborative science — on which this study was the first project [69]. We began the study by publicly proposing the idea and inviting discussion [229]. We continued by chronicling our progress via discussions. We used Thinklab as the frontend to coordinate and report our analyses and GitHub as the backend to host our code, data, and notebooks. On top of our Thinklab team consisting of core contributors, we welcomed community contribution and review. In areas where our expertise was lacking or advice would be helpful, we sought input from domain experts and encouraged them to respond on Thinklab where their comments would be CC BY licensed and their contribution rated and rewarded.

    In total, 40 non-team members commented across 86 discussions, which generated 622 comments and 191 notes (Figure 6). Thinklab content for this project totaled 145,771 words or 918,837 characters [230]. Using an estimated 7,000 words per academic publication as a benchmark, Project Rephetio generated written content comparable in volume to 20.8 publications prior to its completion. We noticed several other benefits from using Thinklab including forging a community of contributors [231]; receiving feedback during the early stages when feedback was most actionable [232]; disseminating our research without delay [233,234]; opening avenues for external input [235]; facilitating problem-oriented teaching [236,237]; and improving our documentation by maintaining a publication-grade digital lab notebook [238].

    -Figure 6: The growth the Project Rephetio corpus on Thinklab over time. This figure shows Project Rephetio contributions by user over time. Each band represented the cumulative contribution of a Thinklab user to discussions in Project Rephetio [230]. Users are ordered by date of first contribution. Users who contributed over 4,500 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.
    Figure 6: The growth the Project Rephetio corpus on Thinklab over time. This figure shows Project Rephetio contributions by user over time. Each band represented the cumulative contribution of a Thinklab user to discussions in Project Rephetio [230]. Users are ordered by date of first contribution. Users who contributed over 4,500 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.
    +Figure 6: The growth the Project Rephetio corpus on Thinklab over time. This figure shows Project Rephetio contributions by user over time. Each band represented the cumulative contribution of a Thinklab user to discussions in Project Rephetio [230]. Users are ordered by date of first contribution. Users who contributed over 4,500 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.
    Figure 6: The growth the Project Rephetio corpus on Thinklab over time. This figure shows Project Rephetio contributions by user over time. Each band represented the cumulative contribution of a Thinklab user to discussions in Project Rephetio [230]. Users are ordered by date of first contribution. Users who contributed over 4,500 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.

    Thinklab began winding down operations in July 2017 and has switched to a static state. While users will no longer be able to add comments, the corpus of content remains browsable at https://think-lab.github.io and available in machine-readable formats at dhimmel/thinklytics.

    The preprint for this study is available at doi.org/bs4f [239]. The manuscript was written in markdown, originally on Thinklab at doi.org/bszr [240]. In August 2017, we switched to using the Manubot system to generate the manuscript. With Manubot, a GitHub repository (dhimmel/rephetio-manuscript) tracks the manuscript’s source code, while continuous integration automatically rebuilds the manuscript upon changes. As a result, the latest version of the manuscript is always available at dhimmel.github.io/rephetio-manuscript. Additionally, readers can leave feedback or questions for the Project Rephetio team via GitHub Issues.

    @@ -945,1223 +945,5105 @@

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    +DOI: 10.5281/zenodo.268654

    42. Predictions of whether a compound treats a disease
    Daniel Himmelstein, Chrissy Hessler, Pouya Khankhanian
    -ThinkLab (2016-05-17) https://doi.org/10.15363/thinklab.d203

    +ThinkLab (2016-05-17) https://doi.org/f3qbmh
    +DOI: 10.15363/thinklab.d203

    43. Development of Novel Pharmacotherapeutics for Tobacco Dependence: Progress and Future Directions
    D. Harmey, P. R. Griffin, P. J. Kenny
    -Nicotine & Tobacco Research (2012-09-27) https://doi.org/10.1093/ntr/nts201

    +Nicotine & Tobacco Research (2012-09-27) https://doi.org/f4crs2
    +DOI: 10.1093/ntr/nts201 · PMID: 23024249 · PMCID: PMC3611986

    44. Varenicline Is a Partial Agonist at  4beta2 and a Full Agonist at  7 Neuronal Nicotinic Receptors
    K. B. Mihalak
    -Molecular Pharmacology (2006-06-20) https://doi.org/10.1124/mol.106.025130

    +Molecular Pharmacology (2006-06-20) https://doi.org/cbmrs6
    +DOI: 10.1124/mol.106.025130 · PMID: 16766716

    45. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease
    Thorgeir E. Thorgeirsson, Frank Geller, Patrick Sulem, Thorunn Rafnar, Anna Wiste, Kristinn P. Magnusson, Andrei Manolescu, Gudmar Thorleifsson, Hreinn Stefansson, Andres Ingason, … Kari Stefansson
    -Nature (2008-04-03) https://doi.org/10.1038/nature06846

    +Nature (2008-04) https://doi.org/bxg8qk
    +DOI: 10.1038/nature06846 · PMID: 18385739 · PMCID: PMC4539558

    46. Evaluation of the safety of bupropion (Zyban) for smoking cessation from experience gained in general practice use in England in 2000
    Andrew Boshier, Lynda V. Wilton, Saad A. W. Shakir
    -European Journal of Clinical Pharmacology (2003-12-01) https://doi.org/10.1007/s00228-003-0693-0

    +European Journal of Clinical Pharmacology (2003-12-01) https://doi.org/b77h82
    +DOI: 10.1007/s00228-003-0693-0 · PMID: 14615857

    47. Efficacy and Safety of Varenicline for Smoking Cessation
    J. Taylor Hays, Jon O. Ebbert, Amit Sood
    -The American Journal of Medicine (2008-04) https://doi.org/10.1016/j.amjmed.2008.01.017

    +The American Journal of Medicine (2008-04) https://doi.org/bp33z7
    +DOI: 10.1016/j.amjmed.2008.01.017 · PMID: 18342165

    48. Nicotine receptor partial agonists for smoking cessation
    Kate Cahill, Nicola Lindson-Hawley, Kyla H Thomas, Thomas R Fanshawe, Tim Lancaster
    -Cochrane Database of Systematic Reviews (2016-05-09) https://doi.org/10.1002/14651858.cd006103.pub7

    +Cochrane Database of Systematic Reviews (2016-05-09) https://doi.org/f8rnz8
    +DOI: 10.1002/14651858.cd006103.pub7 · PMID: 27158893

    49. Placebo-Controlled Trial of Cytisine for Smoking Cessation
    Robert West, Witold Zatonski, Magdalena Cedzynska, Dorota Lewandowska, Joanna Pazik, Paul Aveyard, John Stapleton
    -New England Journal of Medicine (2011-09-29) https://doi.org/10.1056/nejmoa1102035

    +New England Journal of Medicine (2011-09-29) https://doi.org/d69twc
    +DOI: 10.1056/nejmoa1102035 · PMID: 21991893

    50. Cytisine versus Nicotine for Smoking Cessation
    Natalie Walker, Colin Howe, Marewa Glover, Hayden McRobbie, Joanne Barnes, Vili Nosa, Varsha Parag, Bruce Bassett, Christopher Bullen
    -New England Journal of Medicine (2014-12-18) https://doi.org/10.1056/nejmoa1407764

    +New England Journal of Medicine (2014-12-18) https://doi.org/xtz
    +DOI: 10.1056/nejmoa1407764 · PMID: 25517706

    51. Repeated administration of an acetylcholinesterase inhibitor attenuates nicotine taking in rats and smoking behavior in human smokers
    RL Ashare, BA Kimmey, LE Rupprecht, ME Bowers, MR Hayes, HD Schmidt
    -Translational Psychiatry (2016-01-19) https://doi.org/10.1038/tp.2015.209

    +Translational Psychiatry (2016-01) https://doi.org/f78cns
    +DOI: 10.1038/tp.2015.209 · PMID: 26784967 · PMCID: PMC5068882

    52. Prediction in epilepsy
    Pouya Khankhanian, Daniel Himmelstein
    -ThinkLab (2016-09-18) https://doi.org/10.15363/thinklab.d224

    +ThinkLab (2016-09-18) https://doi.org/gbr42f
    +DOI: 10.15363/thinklab.d224

    53. Visualizing the top epilepsy predictions in Cytoscape
    Daniel Himmelstein, Pouya Khankhanian, Alexander Pico, Lars Juhl Jensen, Scooter Morris
    -ThinkLab (2017-01-24) https://doi.org/10.15363/thinklab.d230

    +ThinkLab (2017-01-24) https://doi.org/gbr42r
    +DOI: 10.15363/thinklab.d230

    54. Treatment of Refractory Status Epilepticus With Inhalational Anesthetic Agents Isoflurane and Desflurane
    Seyed M. Mirsattari, Michael D. Sharpe, G. Bryan Young
    -Archives of Neurology (2004-08-01) https://doi.org/10.1001/archneur.61.8.1254

    +Archives of Neurology (2004-08-01) https://doi.org/b648br
    +DOI: 10.1001/archneur.61.8.1254 · PMID: 15313843

    55. Anatomical Therapeutic Chemical Classification System (WHO)
    Karen Knaus
    -The SAGE Encyclopedia of Pharmacology and Society (2016-03-29) https://doi.org/10.4135/9781483349985.n37

    +The SAGE Encyclopedia of Pharmacology and Society (2016-03-29) https://doi.org/10.4135/9781483349985.n37
    +DOI: 10.4135/9781483349985.n37 · ISBN: 9781483350004

    56. Antiepileptic Drug Interactions - Principles and Clinical Implications
    Svein I. Johannessen, Cecilie Johannessen Landmark
    -Current Neuropharmacology (2010-09-01) https://doi.org/10.2174/157015910792246254

    +Current Neuropharmacology (2010-09-01) https://doi.org/cvc5z8
    +DOI: 10.2174/157015910792246254 · PMID: 21358975 · PMCID: PMC3001218

    57. The neurobiology of antiepileptic drugs
    Michael A. Rogawski, Wolfgang Löscher
    -Nature Reviews Neuroscience (2004-07) https://doi.org/10.1038/nrn1430

    +Nature Reviews Neuroscience (2004-07) https://doi.org/fvwhnm
    +DOI: 10.1038/nrn1430 · PMID: 15208697

    58. Proconvulsant effects of antidepressants — What is the current evidence?
    Cecilie Johannessen Landmark, Oliver Henning, Svein I. Johannessen
    -Epilepsy & Behavior (2016-08) https://doi.org/10.1016/j.yebeh.2016.01.029

    +Epilepsy & Behavior (2016-08) https://doi.org/f8zscw
    +DOI: 10.1016/j.yebeh.2016.01.029 · PMID: 26926001

    59. Why we predicted ictogenic tricyclic compounds treat epilepsy?
    Daniel Himmelstein
    -ThinkLab (2017-03-10) https://doi.org/10.15363/thinklab.d231

    +ThinkLab (2017-03-10) https://doi.org/gbr42k
    +DOI: 10.15363/thinklab.d231

    60. Antidepressants and seizures: Clinical anecdotes overshadow neuroscience
    John W. Dailey, Dean K. Naritoku
    -Biochemical Pharmacology (1996-11) https://doi.org/10.1016/s0006-2952(96)00509-6

    +Biochemical Pharmacology (1996-11) https://doi.org/dfhvch
    +DOI: 10.1016/s0006-2952(96)00509-6

    61. Movement disorders in patients taking anticonvulsants
    C Zadikoff, RP Munhoz, AN Asante, N Politzer, R Wennberg, P Carlen, A Lang
    -Journal of Neurology, Neurosurgery & Psychiatry (2007-02-01) https://doi.org/10.1136/jnnp.2006.100222

    +Journal of Neurology, Neurosurgery & Psychiatry (2007-02-01) https://doi.org/fpvxgq
    +DOI: 10.1136/jnnp.2006.100222 · PMID: 17012337 · PMCID: PMC2077655

    62. Anticonvulsant-induced downbeat nystagmus in epilepsy
    Dongyan Wu, Roland D. Thijs
    -Epilepsy & Behavior Case Reports (2015) https://doi.org/10.1016/j.ebcr.2015.07.003

    +Epilepsy & Behavior Case Reports (2015) https://doi.org/gbr4z9
    +DOI: 10.1016/j.ebcr.2015.07.003 · PMID: 26543808 · PMCID: PMC4556747

    63. The effect of antiepileptic drugs on visual performance
    Emma J Roff Hilton, Sarah L Hosking, Tim Betts
    -Seizure (2003-05-30) https://doi.org/10.1016/s1059-1311(03)00082-7

    +Seizure (2003-05-30) https://doi.org/10.1016/s1059-1311(03)00082-7
    +DOI: 10.1016/s1059-1311(03)00082-7

    64. Effect of antiepileptic drugs on sleep
    Fabio Placidi, Anna Scalise, Maria Grazia Marciani, Andrea Romigi, Marina Diomedi, Gian Luigi Gigli
    -Clinical Neurophysiology (2000-09) https://doi.org/10.1016/s1388-2457(00)00411-9

    +Clinical Neurophysiology (2000-09) https://doi.org/b5ztp7
    +DOI: 10.1016/s1388-2457(00)00411-9

    65. Gastrointestinal adverse effects of antiepileptic drugs in intractable epileptic patients
    Soodeh Razeghi Jahromi, Mansoureh Togha, Sohrab Hashemi Fesharaki, Masoumeh Najafi, Nahid Beladi Moghadam, Jalil Arab Kheradmand, Hadi Kazemi, Ali Gorji
    -Seizure (2011-05) https://doi.org/10.1016/j.seizure.2010.12.011

    +Seizure (2011-05) https://doi.org/chk4dn
    +DOI: 10.1016/j.seizure.2010.12.011 · PMID: 21236703

    66. Methods for biological data integration: perspectives and challenges
    Vladimir Gligorijević, Nataša Pržulj
    -Journal of The Royal Society Interface (2015-10-21) https://doi.org/10.1098/rsif.2015.0571

    +Journal of The Royal Society Interface (2015-10-21) https://doi.org/bdzp
    +DOI: 10.1098/rsif.2015.0571 · PMID: 26490630 · PMCID: PMC4685837

    67. Multilayer networks
    M. Kivela, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, M. A. Porter
    -Journal of Complex Networks (2014-07-14) https://doi.org/10.1093/comnet/cnu016

    +Journal of Complex Networks (2014-07-14) https://doi.org/f3mn4x
    +DOI: 10.1093/comnet/cnu016

    68. Renaming “heterogeneous networks” to a more concise and catchy term
    Daniel Himmelstein, Casey Greene, Sergio Baranzini
    -ThinkLab (2015-08-16) https://doi.org/10.15363/thinklab.d104

    +ThinkLab (2015-08-16) https://doi.org/f3mn4v
    +DOI: 10.15363/thinklab.d104

    69. Rephetio: Repurposing drugs on a hetnet [project]
    Daniel Himmelstein, Antoine Lizee, Chrissy Hessler, Leo Brueggeman, Sabrina Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio Baranzini
    -ThinkLab (2015-01-12) https://doi.org/10.15363/thinklab.4

    +ThinkLab (2015-01-12) https://doi.org/993
    +DOI: 10.15363/thinklab.4

    70. Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data
    M. Sirota, J. T. Dudley, J. Kim, A. P. Chiang, A. A. Morgan, A. Sweet-Cordero, J. Sage, A. J. Butte
    -Science Translational Medicine (2011-08-17) https://doi.org/10.1126/scitranslmed.3001318

    +Science Translational Medicine (2011-08-17) https://doi.org/c3fwxv
    +DOI: 10.1126/scitranslmed.3001318 · PMID: 21849665 · PMCID: PMC3502016

    71. Acamprosate attenuates the handling induced convulsions during alcohol withdrawal in Swiss Webster mice
    Justin M. Farook, Ali Krazem, Ben Lewis, Dennis J. Morrell, John M. Littleton, Susan Barron
    -Physiology & Behavior (2008-09) https://doi.org/10.1016/j.physbeh.2008.05.020

    +Physiology & Behavior (2008-09) https://doi.org/cnfsgc
    +DOI: 10.1016/j.physbeh.2008.05.020 · PMID: 18577392 · PMCID: PMC2561203

    72. Data programming with DDLite
    Henry R. Ehrenberg, Jaeho Shin, Alexander J. Ratner, Jason A. Fries, Christopher Ré
    -Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA ’16 (2016) https://doi.org/10.1145/2939502.2939515

    +Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA ’16 (2016) https://doi.org/gbr42c
    +DOI: 10.1145/2939502.2939515

    73. Brainstorming future directions for Hetionet
    Daniel Himmelstein, Benjamin Good, Pouya Khankhanian, Alex Ratner
    -ThinkLab (2016-11-19) https://doi.org/10.15363/thinklab.d227

    +ThinkLab (2016-11-19) https://doi.org/gbr42g
    +DOI: 10.15363/thinklab.d227

    74. English, Chinese and ER diagrams
    Peter Pin-Shan Chen
    -Data & Knowledge Engineering (1997-06) https://doi.org/10.1016/s0169-023x(97)00017-7

    +Data & Knowledge Engineering (1997-06) https://doi.org/c7mk3q
    +DOI: 10.1016/s0169-023x(97)00017-7

    75. Data nomenclature: naming and abbreviating our network types
    Daniel Himmelstein, Lars Juhl Jensen, Pouya Khankhanian
    -ThinkLab (2016-02-17) https://doi.org/10.15363/thinklab.d162

    +ThinkLab (2016-02-17) https://doi.org/f3qbmc
    +DOI: 10.15363/thinklab.d162

    76. Ten Simple Rules for Selecting a Bio-ontology
    James Malone, Robert Stevens, Simon Jupp, Tom Hancocks, Helen Parkinson, Cath Brooksbank
    -PLOS Computational Biology (2016-02-11) https://doi.org/10.1371/journal.pcbi.1004743

    +PLOS Computational Biology (2016-02-11) https://doi.org/f3mn59
    +DOI: 10.1371/journal.pcbi.1004743 · PMID: 26867217 · PMCID: PMC4750991

    77. Disease Ontology: a backbone for disease semantic integration
    L. M. Schriml, C. Arze, S. Nadendla, Y.-W. W. Chang, M. Mazaitis, V. Felix, G. Feng, W. A. Kibbe
    -Nucleic Acids Research (2011-11-12) https://doi.org/10.1093/nar/gkr972

    +Nucleic Acids Research (2011-11-12) https://doi.org/fwvx82
    +DOI: 10.1093/nar/gkr972 · PMID: 22080554 · PMCID: PMC3245088

    78. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data
    -W. A. Kibbe, C. Arze, V. Felix, E. Mitraka, E. Bolton, G. Fu, C. J. Mungall, J. X. Binder, J. Malone, D. Vasant, … L. M. Schriml
    -Nucleic Acids Research (2014-10-27) https://doi.org/10.1093/nar/gku1011

    +Warren A. Kibbe, Cesar Arze, Victor Felix, Elvira Mitraka, Evan Bolton, Gang Fu, Christopher J. Mungall, Janos X. Binder, James Malone, Drashtti Vasant, … Lynn M. Schriml
    +Nucleic Acids Research (2014-10-27) https://doi.org/f3mn6b
    +DOI: 10.1093/nar/gku1011 · PMID: 25348409 · PMCID: PMC4383880

    79. Unifying disease vocabularies
    Daniel Himmelstein, Tong Shu Li
    -ThinkLab (2015-03-30) https://doi.org/10.15363/thinklab.d44

    +ThinkLab (2015-03-30) https://doi.org/f3mqv5
    +DOI: 10.15363/thinklab.d44

    80. User-Friendly Extensions To The Disease Ontology V1.0
    Daniel S. Himmelstein
    -Zenodo (2016-02-04) https://doi.org/10.5281/zenodo.45584

    +Zenodo (2016-02-04) https://doi.org/f3mqvc
    +DOI: 10.5281/zenodo.45584

    81. Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis
    T.-J. Wu, L. M. Schriml, Q.-R. Chen, M. Colbert, D. J. Crichton, R. Finney, Y. Hu, W. A. Kibbe, H. Kincaid, D. Meerzaman, … R. Mazumder
    -Database (2015-04-04) https://doi.org/10.1093/database/bav032

    +Database (2015-04-04) https://doi.org/f3mn6c
    +DOI: 10.1093/database/bav032 · PMID: 25841438 · PMCID: PMC4385274

    82. Mining knowledge from MEDLINE articles and their indexed MeSH terms
    Daniel Himmelstein, Alex Pankov
    -ThinkLab (2015-05-10) https://doi.org/10.15363/thinklab.d67

    +ThinkLab (2015-05-10) https://doi.org/f3mqwp
    +DOI: 10.15363/thinklab.d67

    83. User-Friendly Extensions To Mesh V1.0
    Daniel S. Himmelstein
    -Zenodo (2016-02-04) https://doi.org/10.5281/zenodo.45586

    +Zenodo (2016-02-04) https://doi.org/f3mqvt
    +DOI: 10.5281/zenodo.45586

    84. DrugBank 4.0: shedding new light on drug metabolism
    Vivian Law, Craig Knox, Yannick Djoumbou, Tim Jewison, An Chi Guo, Yifeng Liu, Adam Maciejewski, David Arndt, Michael Wilson, Vanessa Neveu, … David S. Wishart
    -Nucleic Acids Research (2013-11-06) https://doi.org/10.1093/nar/gkt1068

    +Nucleic Acids Research (2013-11-06) https://doi.org/f3mn6d
    +DOI: 10.1093/nar/gkt1068 · PMID: 24203711 · PMCID: PMC3965102

    85. Unifying drug vocabularies
    Daniel Himmelstein
    -ThinkLab (2015-03-16) https://doi.org/10.15363/thinklab.d40

    +ThinkLab (2015-03-16) https://doi.org/f3mqtx
    +DOI: 10.15363/thinklab.d40

    86. User-Friendly Extensions Of The Drugbank Database V1.0
    Daniel S. Himmelstein
    -Zenodo (2016-02-04) https://doi.org/10.5281/zenodo.45579

    +Zenodo (2016-02-04) https://doi.org/f3mqwk
    +DOI: 10.5281/zenodo.45579

    87. The SIDER database of drugs and side effects
    Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, Peer Bork
    -Nucleic Acids Research (2015-10-19) https://doi.org/10.1093/nar/gkv1075

    +Nucleic Acids Research (2015-10-19) https://doi.org/f3mn6f
    +DOI: 10.1093/nar/gkv1075 · PMID: 26481350 · PMCID: PMC4702794

    88. Extracting side effects from SIDER 4
    Daniel Himmelstein
    -ThinkLab (2015-08-08) https://doi.org/10.15363/thinklab.d97

    +ThinkLab (2015-08-08) https://doi.org/f3mqvd
    +DOI: 10.15363/thinklab.d97

    89. Extracting Tidy And User-Friendly Tsvs From Sider 4.1
    Daniel S. Himmelstein
    -Zenodo (2016-02-03) https://doi.org/10.5281/zenodo.45521

    +Zenodo (2016-02-03) https://doi.org/f3mqwd
    +DOI: 10.5281/zenodo.45521

    90. The Unified Medical Language System (UMLS): integrating biomedical terminology
    O. Bodenreider
    -Nucleic Acids Research (2004-01-01) https://doi.org/10.1093/nar/gkh061

    +Nucleic Acids Research (2004-01-01) https://doi.org/bzs9ps
    +DOI: 10.1093/nar/gkh061 · PMID: 14681409 · PMCID: PMC308795

    91. DrugCentral: online drug compendium
    Oleg Ursu, Jayme Holmes, Jeffrey Knockel, Cristian G. Bologa, Jeremy J. Yang, Stephen L. Mathias, Stuart J. Nelson, Tudor I. Oprea
    -Nucleic Acids Research (2016-10-26) https://doi.org/10.1093/nar/gkw993

    +Nucleic Acids Research (2016-10-26) https://doi.org/f9vphz
    +DOI: 10.1093/nar/gkw993 · PMID: 27789690 · PMCID: PMC5210665

    92. Incorporating DrugCentral data in our network
    Daniel Himmelstein, Oleg Ursu, Mike Gilson, Pouya Khankhanian, Tudor Oprea
    -ThinkLab (2016-03-20) https://doi.org/10.15363/thinklab.d186

    +ThinkLab (2016-03-20) https://doi.org/f3mqtn
    +DOI: 10.15363/thinklab.d186

    93. Entrez Gene: gene-centered information at NCBI
    D. Maglott, J. Ostell, K. D. Pruitt, T. Tatusova
    -Nucleic Acids Research (2010-11-28) https://doi.org/10.1093/nar/gkq1237

    +Nucleic Acids Research (2010-11-28) https://doi.org/fsjcqz
    +DOI: 10.1093/nar/gkq1237 · PMID: 21115458 · PMCID: PMC3013746

    94. Using Entrez Gene as our gene vocabulary
    Daniel Himmelstein, Casey Greene, Alexander Pico
    -ThinkLab (2015-02-27) https://doi.org/10.15363/thinklab.d34

    +ThinkLab (2015-02-27) https://doi.org/f3mqv4
    +DOI: 10.15363/thinklab.d34

    95. Processed Entrez Gene Datasets For Humans V1.0
    Daniel S. Himmelstein
    -Zenodo (2016-02-04) https://doi.org/10.5281/zenodo.45524

    +Zenodo (2016-02-04) https://doi.org/f3mqvz
    +DOI: 10.5281/zenodo.45524

    96. Uberon, an integrative multi-species anatomy ontology
    Christopher J Mungall, Carlo Torniai, Georgios V Gkoutos, Suzanna E Lewis, Melissa A Haendel
    -Genome Biology (2012) https://doi.org/10.1186/gb-2012-13-1-r5

    +Genome Biology (2012) https://doi.org/fxx6qr
    +DOI: 10.1186/gb-2012-13-1-r5 · PMID: 22293552 · PMCID: PMC3334586

    97. Tissue Node
    Venkat Malladi, Daniel Himmelstein, Chris Mungall
    -ThinkLab (2015-03-19) https://doi.org/10.15363/thinklab.d41

    +ThinkLab (2015-03-19) https://doi.org/f3mn6g
    +DOI: 10.15363/thinklab.d41

    98. User-Friendly Anatomical Structures Data From The Uberon Ontology V1.0
    Daniel S. Himmelstein
    -Zenodo (2016-02-04) https://doi.org/10.5281/zenodo.45527

    +Zenodo (2016-02-04) https://doi.org/f3mqtt
    +DOI: 10.5281/zenodo.45527

    99. WikiPathways: capturing the full diversity of pathway knowledge
    Martina Kutmon, Anders Riutta, Nuno Nunes, Kristina Hanspers, Egon L. Willighagen, Anwesha Bohler, Jonathan Mélius, Andra Waagmeester, Sravanthi R. Sinha, Ryan Miller, … Alexander R. Pico
    -Nucleic Acids Research (2015-10-19) https://doi.org/10.1093/nar/gkv1024

    +Nucleic Acids Research (2015-10-19) https://doi.org/f3mn6h
    +DOI: 10.1093/nar/gkv1024 · PMID: 26481357 · PMCID: PMC4702772

    100. WikiPathways: Pathway Editing for the People
    Alexander R Pico, Thomas Kelder, Martijn P van Iersel, Kristina Hanspers, Bruce R Conklin, Chris Evelo
    -PLoS Biology (2008-07-22) https://doi.org/10.1371/journal.pbio.0060184

    +PLoS Biology (2008-07-22) https://doi.org/bhsdc2
    +DOI: 10.1371/journal.pbio.0060184 · PMID: 18651794 · PMCID: PMC2475545

    101. The Reactome pathway Knowledgebase
    Antonio Fabregat, Konstantinos Sidiropoulos, Phani Garapati, Marc Gillespie, Kerstin Hausmann, Robin Haw, Bijay Jassal, Steven Jupe, Florian Korninger, Sheldon McKay, … Peter D’Eustachio
    -Nucleic Acids Research (2015-12-09) https://doi.org/10.1093/nar/gkv1351

    +Nucleic Acids Research (2015-12-09) https://doi.org/f3mn6j
    +DOI: 10.1093/nar/gkv1351 · PMID: 26656494 · PMCID: PMC4702931

    102. PID: the Pathway Interaction Database
    Carl F. Schaefer, Kira Anthony, Shiva Krupa, Jeffrey Buchoff, Matthew Day, Timo Hannay, Kenneth H. Buetow
    -Nucleic Acids Research (2008-10-02) https://doi.org/10.1093/nar/gkn653

    +Nucleic Acids Research (2008-10-02) https://doi.org/dv62wn
    +DOI: 10.1093/nar/gkn653 · PMID: 18832364 · PMCID: PMC2686461

    103. Pathway Commons, a web resource for biological pathway data
    E. G. Cerami, B. E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, N. Schultz, G. D. Bader, C. Sander
    -Nucleic Acids Research (2010-11-10) https://doi.org/10.1093/nar/gkq1039

    +Nucleic Acids Research (2010-11-10) https://doi.org/csjvsx
    +DOI: 10.1093/nar/gkq1039 · PMID: 21071392 · PMCID: PMC3013659

    104. Adding pathway resources to your network
    Alexander Pico, Daniel Himmelstein
    -ThinkLab (2015-06-08) https://doi.org/10.15363/thinklab.d72

    +ThinkLab (2015-06-08) https://doi.org/f3mn6k
    +DOI: 10.15363/thinklab.d72

    105. Dhimmel/Pathways V2.0: Compiling Human Pathway Gene Sets
    Daniel S. Himmelstein, Alexander R. Pico
    -Zenodo (2016-04-02) https://doi.org/10.5281/zenodo.48810

    +Zenodo (2016-04-02) https://doi.org/bfq3
    +DOI: 10.5281/zenodo.48810

    106. Gene Ontology: tool for the unification of biology
    Michael Ashburner, Catherine A. Ball, Judith A. Blake, David Botstein, Heather Butler, J. Michael Cherry, Allan P. Davis, Kara Dolinski, Selina S. Dwight, Janan T. Eppig, … Gavin Sherlock
    -Nature Genetics (2000-05) https://doi.org/10.1038/75556

    +Nature Genetics (2000-05) https://doi.org/b9gp96
    +DOI: 10.1038/75556 · PMID: 10802651 · PMCID: PMC3037419

    107. Disease Ontology feature requests
    Daniel Himmelstein
    -ThinkLab (2015-05-11) https://doi.org/10.15363/thinklab.d68

    +ThinkLab (2015-05-11) https://doi.org/f3mqvf
    +DOI: 10.15363/thinklab.d68

    108. Chemical databases: curation or integration by user-defined equivalence?
    Anne Hersey, Jon Chambers, Louisa Bellis, A. Patrícia Bento, Anna Gaulton, John P. Overington
    -Drug Discovery Today: Technologies (2015-07) https://doi.org/10.1016/j.ddtec.2015.01.005

    +Drug Discovery Today: Technologies (2015-07) https://doi.org/f3mn6m
    +DOI: 10.1016/j.ddtec.2015.01.005 · PMID: 26194583 · PMCID: PMC6294287

    109. UniChem: a unified chemical structure cross-referencing and identifier tracking system
    Jon Chambers, Mark Davies, Anna Gaulton, Anne Hersey, Sameer Velankar, Robert Petryszak, Janna Hastings, Louisa Bellis, Shaun McGlinchey, John P Overington
    -Journal of Cheminformatics (2013) https://doi.org/10.1186/1758-2946-5-3

    +Journal of Cheminformatics (2013-01-14) https://doi.org/f3mn6n
    +DOI: 10.1186/1758-2946-5-3 · PMID: 23317286 · PMCID: PMC3616875

    110. UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers
    Jon Chambers, Mark Davies, Anna Gaulton, George Papadatos, Anne Hersey, John P Overington
    -Journal of Cheminformatics (2014-09-04) https://doi.org/10.1186/s13321-014-0043-5

    +Journal of Cheminformatics (2014-09-04) https://doi.org/vkq
    +DOI: 10.1186/s13321-014-0043-5 · PMID: 25221628 · PMCID: PMC4158273

    111. InChI - the worldwide chemical structure identifier standard
    Stephen Heller, Alan McNaught, Stephen Stein, Dmitrii Tchekhovskoi, Igor Pletnev
    -Journal of Cheminformatics (2013) https://doi.org/10.1186/1758-2946-5-7

    +Journal of Cheminformatics (2013-01-24) https://doi.org/6bg
    +DOI: 10.1186/1758-2946-5-7 · PMID: 23343401 · PMCID: PMC3599061

    112. Dhimmel/Bgee V1.0: Anatomy-Specific Gene Expression In Humans From Bgee
    Daniel Himmelstein, Frederic Bastian, Sergio Baranzini
    -Zenodo (2016-03-08) https://doi.org/10.5281/zenodo.47157

    +Zenodo (2016-03-08) https://doi.org/f3mqv2
    +DOI: 10.5281/zenodo.47157

    113. Processing Bgee for tissue-specific gene presence and over/under-expression
    Daniel Himmelstein, Frederic Bastian
    -ThinkLab (2015-11-03) https://doi.org/10.15363/thinklab.d124

    +ThinkLab (2015-11-03) https://doi.org/f3mqwg
    +DOI: 10.15363/thinklab.d124

    114. Tissue-specific gene expression resources
    Daniel Himmelstein, Frederic Bastian
    -ThinkLab (2015-06-17) https://doi.org/10.15363/thinklab.d81

    +ThinkLab (2015-06-17) https://doi.org/f3mqvb
    +DOI: 10.15363/thinklab.d81

    115. Bgee: Integrating and Comparing Heterogeneous Transcriptome Data Among Species
    Frederic Bastian, Gilles Parmentier, Julien Roux, Sebastien Moretti, Vincent Laudet, Marc Robinson-Rechavi
    -Lecture Notes in Computer Science (2008-06) https://doi.org/10.1007/978-3-540-69828-9_12

    +Lecture Notes in Computer Science (2008-06) https://doi.org/10.1007/978-3-540-69828-9_12
    +DOI: 10.1007/978-3-540-69828-9_12

    116. Comprehensive comparison of large-scale tissue expression datasets
    Alberto Santos, Kalliopi Tsafou, Christian Stolte, Sune Pletscher-Frankild, Seán I. O’Donoghue, Lars Juhl Jensen
    -PeerJ (2015-06-30) https://doi.org/10.7717/peerj.1054

    +PeerJ (2015-06-30) https://doi.org/f3mn6p
    +DOI: 10.7717/peerj.1054 · PMID: 26157623 · PMCID: PMC4493645

    117. Gene–Tissue Relationships From The Tissues Database
    Daniel Himmelstein, Lars Juhl Jensen
    -Zenodo (2015-08-09) https://doi.org/10.5281/zenodo.27244

    +Zenodo (2015-08-09) https://doi.org/f3mqv8
    +DOI: 10.5281/zenodo.27244

    118. The TISSUES resource for the tissue-specificity of genes
    Daniel Himmelstein, Lars Juhl Jensen
    -ThinkLab (2015-07-10) https://doi.org/10.15363/thinklab.d91

    +ThinkLab (2015-07-10) https://doi.org/f3mqwf
    +DOI: 10.15363/thinklab.d91

    119. BindingDB: A Web-Accessible Molecular Recognition Database
    Xi Chen, Ming Liu, Michael Gilson
    -Combinatorial Chemistry & High Throughput Screening (2001-12-01) https://doi.org/10.2174/1386207013330670

    +Combinatorial Chemistry & High Throughput Screening (2001-12-01) https://doi.org/f3mn6q
    +DOI: 10.2174/1386207013330670

    120. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology
    Michael K. Gilson, Tiqing Liu, Michael Baitaluk, George Nicola, Linda Hwang, Jenny Chong
    -Nucleic Acids Research (2015-10-19) https://doi.org/10.1093/nar/gkv1072

    +Nucleic Acids Research (2015-10-19) https://doi.org/f3mn6r
    +DOI: 10.1093/nar/gkv1072 · PMID: 26481362 · PMCID: PMC4702793

    121. DrugBank: a comprehensive resource for in silico drug discovery and exploration
    D. S. Wishart
    -Nucleic Acids Research (2006-01-01) https://doi.org/10.1093/nar/gkj067

    +Nucleic Acids Research (2006-01-01) https://doi.org/c7cp42
    +DOI: 10.1093/nar/gkj067 · PMID: 16381955 · PMCID: PMC1347430

    122. Integrating drug target information from BindingDB
    Daniel Himmelstein, Mike Gilson
    -ThinkLab (2015-04-13) https://doi.org/10.15363/thinklab.d53

    +ThinkLab (2015-04-13) https://doi.org/f3mqvv
    +DOI: 10.15363/thinklab.d53

    123. Processing The October 2015 Bindingdb
    Daniel Himmelstein, Michael Gilson, Sergio Baranzini
    -Zenodo (2015-11-19) https://doi.org/10.5281/zenodo.33987

    +Zenodo (2015-11-19) https://doi.org/f3mqvp
    +DOI: 10.5281/zenodo.33987

    124. Protein (target, carrier, transporter, and enzyme) interactions in DrugBank
    Daniel Himmelstein, Sabrina Chen
    -ThinkLab (2015-05-09) https://doi.org/10.15363/thinklab.d65

    +ThinkLab (2015-05-09) https://doi.org/f3mqvm
    +DOI: 10.15363/thinklab.d65

    125. Calculating molecular similarities between DrugBank compounds
    Daniel Himmelstein, Sabrina Chen
    -ThinkLab (2015-05-18) https://doi.org/10.15363/thinklab.d70

    +ThinkLab (2015-05-18) https://doi.org/f3mqwr
    +DOI: 10.15363/thinklab.d70

    126. Pairwise molecular similarities between DrugBank compounds
    Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini
    -Figshare (2015) https://doi.org/10.6084/m9.figshare.1418386

    +Figshare (2015) https://doi.org/f3mqt2
    +DOI: 10.6084/m9.figshare.1418386

    127. Measures of the Amount of Ecologic Association Between Species
    Lee R. Dice
    -Ecology (1945-07) https://doi.org/10.2307/1932409

    +Ecology (1945-07) https://doi.org/dsb8pd
    +DOI: 10.2307/1932409

    128. Extended-Connectivity Fingerprints
    David Rogers, Mathew Hahn
    -Journal of Chemical Information and Modeling (2010-05-24) https://doi.org/10.1021/ci100050t

    +Journal of Chemical Information and Modeling (2010-04-28) https://doi.org/fp3ctj
    +DOI: 10.1021/ci100050t · PMID: 20426451

    129. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service.
    H. L. Morgan
    -Journal of Chemical Documentation (1965-05) https://doi.org/10.1021/c160017a018

    +Journal of Chemical Documentation (1965-05) https://doi.org/bpzn7w
    +DOI: 10.1021/c160017a018

    130. Dhimmel/Gwas-Catalog V1.0: Extracting Gene–Disease Associations From The Gwas Catalog
    Daniel S. Himmelstein, Sergio E. Baranzini
    -Zenodo (2016-03-26) https://doi.org/10.5281/zenodo.48428

    +Zenodo (2016-03-26) https://doi.org/f3mqws
    +DOI: 10.5281/zenodo.48428

    131. Processing the DISEASES resource for disease–gene relationships
    Daniel Himmelstein, Lars Juhl Jensen
    -ThinkLab (2015-08-20) https://doi.org/10.15363/thinklab.d106

    +ThinkLab (2015-08-20) https://doi.org/f3mqt3
    +DOI: 10.15363/thinklab.d106

    132. Dhimmel/Diseases V1.0: Processing The Diseases Database Of Gene–Disease Associations
    Daniel S. Himmelstein, Lars Juhl Jensen
    -Zenodo (2016-03-26) https://doi.org/10.5281/zenodo.48425

    +Zenodo (2016-03-26) https://doi.org/f3mqv7
    +DOI: 10.5281/zenodo.48425

    133. Processing DisGeNET for disease-gene relationships
    Daniel Himmelstein, janet piñero
    -ThinkLab (2015-08-17) https://doi.org/10.15363/thinklab.d105

    +ThinkLab (2015-08-17) https://doi.org/f3mqv3
    +DOI: 10.15363/thinklab.d105

    134. Dhimmel/Disgenet V1.0: Processing The Disgenet Database Of Gene–Disease Associations
    Daniel S. Himmelstein, Janet Piñero
    -Zenodo (2016-03-26) https://doi.org/10.5281/zenodo.48426

    +Zenodo (2016-03-26) https://doi.org/f3mqwt
    +DOI: 10.5281/zenodo.48426

    135. Functional disease annotations for genes using DOAF
    Daniel Himmelstein
    -ThinkLab (2015-07-14) https://doi.org/10.15363/thinklab.d94

    +ThinkLab (2015-07-14) https://doi.org/f3mqvw
    +DOI: 10.15363/thinklab.d94

    136. Dhimmel/Doaf V1.0: Processing The Doaf Database Of Gene–Disease Associations
    Daniel S. Himmelstein
    -Zenodo (2016-03-26) https://doi.org/10.5281/zenodo.48427

    +Zenodo (2016-03-26) https://doi.org/f3mqwx
    +DOI: 10.5281/zenodo.48427

    137. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog)
    Jacqueline MacArthur, Emily Bowler, Maria Cerezo, Laurent Gil, Peggy Hall, Emma Hastings, Heather Junkins, Aoife McMahon, Annalisa Milano, Joannella Morales, … Helen Parkinson
    -Nucleic Acids Research (2016-11-29) https://doi.org/10.1093/nar/gkw1133

    +Nucleic Acids Research (2016-11-29) https://doi.org/f9v7cp
    +DOI: 10.1093/nar/gkw1133 · PMID: 27899670 · PMCID: PMC5210590

    138. Extracting disease-gene associations from the GWAS Catalog
    Daniel Himmelstein
    -ThinkLab (2015-06-16) https://doi.org/10.15363/thinklab.d80

    +ThinkLab (2015-06-16) https://doi.org/f3mqv6
    +DOI: 10.15363/thinklab.d80

    139. Calculating genomic windows for GWAS lead SNPs
    Daniel Himmelstein, Marina Sirota, Greg Way
    -ThinkLab (2015-06-08) https://doi.org/10.15363/thinklab.d71

    +ThinkLab (2015-06-08) https://doi.org/f3mqt8
    +DOI: 10.15363/thinklab.d71

    140. DISEASES: Text mining and data integration of disease–gene associations
    Sune Pletscher-Frankild, Albert Pallejà, Kalliopi Tsafou, Janos X. Binder, Lars Juhl Jensen
    -Methods (2015-03) https://doi.org/10.1016/j.ymeth.2014.11.020

    +Methods (2015-03) https://doi.org/f3mn6s
    +DOI: 10.1016/j.ymeth.2014.11.020 · PMID: 25484339

    141. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes
    J. Pinero, N. Queralt-Rosinach, A. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, F. Sanz, L. I. Furlong
    -Database (2015-04-15) https://doi.org/10.1093/database/bav028

    +Database (2015-04-15) https://doi.org/f3mn6t
    +DOI: 10.1093/database/bav028 · PMID: 25877637 · PMCID: PMC4397996

    142. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants
    Janet Piñero, Àlex Bravo, Núria Queralt-Rosinach, Alba Gutiérrez-Sacristán, Jordi Deu-Pons, Emilio Centeno, Javier García-García, Ferran Sanz, Laura I. Furlong
    -Nucleic Acids Research (2016-10-19) https://doi.org/10.1093/nar/gkw943

    +Nucleic Acids Research (2016-10-19) https://doi.org/f9v9wp
    +DOI: 10.1093/nar/gkw943 · PMID: 27924018 · PMCID: PMC5210640

    143. A Framework for Annotating Human Genome in Disease Context
    Wei Xu, Huisong Wang, Wenqing Cheng, Dong Fu, Tian Xia, Warren A. Kibbe, Simon M. Lin
    -PLoS ONE (2012-12-10) https://doi.org/10.1371/journal.pone.0049686

    +PLoS ONE (2012-12-10) https://doi.org/f3mn6v
    +DOI: 10.1371/journal.pone.0049686 · PMID: 23251346 · PMCID: PMC3519466

    144. STARGEO: expression signatures for disease using crowdsourced GEO annotation
    Daniel Himmelstein, Frederic Bastian, Dexter Hadley, Casey Greene
    -ThinkLab (2015-07-28) https://doi.org/10.15363/thinklab.d96

    +ThinkLab (2015-07-28) https://doi.org/f3mqwh
    +DOI: 10.15363/thinklab.d96

    145. Dhimmel/Stargeo V1.0: Differentially Expressed Genes For 48 Diseases From Stargeo
    Daniel Himmelstein, Dexter Hadley, Alexander Schepanovski
    -Zenodo (2016-03-03) https://doi.org/10.5281/zenodo.46866

    +Zenodo (2016-03-03) https://doi.org/f3mqvg
    +DOI: 10.5281/zenodo.46866

    146. Dhimmel/Medline V1.0: Disease, Symptom, And Anatomy Cooccurence In Medline
    Daniel S. Himmelstein
    -Zenodo (2016-03-28) https://doi.org/10.5281/zenodo.48445

    +Zenodo (2016-03-28) https://doi.org/f3mqts
    +DOI: 10.5281/zenodo.48445

    147. Disease similarity from MEDLINE topic cooccurrence
    Daniel Himmelstein
    -ThinkLab (2015-07-14) https://doi.org/10.15363/thinklab.d93

    +ThinkLab (2015-07-14) https://doi.org/f3mqvx
    +DOI: 10.15363/thinklab.d93

    148. On the Interpretation of χ 2 from Contingency Tables, and the Calculation of P
    R. A. Fisher
    -Journal of the Royal Statistical Society (1922-01) https://doi.org/10.2307/2340521

    +Journal of the Royal Statistical Society (1922-01) https://doi.org/frpswx
    +DOI: 10.2307/2340521

    149. Computing consensus transcriptional profiles for LINCS L1000 perturbations
    Daniel Himmelstein, Caty Chung
    -ThinkLab (2015-03-26) https://doi.org/10.15363/thinklab.d43

    +ThinkLab (2015-03-26) https://doi.org/f3mqwc
    +DOI: 10.15363/thinklab.d43

    150. Consensus signatures for LINCS L1000 perturbations
    Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini
    -Figshare (2016) https://doi.org/10.6084/m9.figshare.3085426.v1

    +Figshare (2016) https://doi.org/f3mqvs
    +DOI: 10.6084/m9.figshare.3085426.v1

    151. Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks
    Nolan Priedigkeit, Nicholas Wolfe, Nathan L. Clark
    -PLOS Genetics (2015-02-13) https://doi.org/10.1371/journal.pgen.1004967

    +PLOS Genetics (2015-02-13) https://doi.org/f3mn6w
    +DOI: 10.1371/journal.pgen.1004967 · PMID: 25679399 · PMCID: PMC4334549

    152. Selecting informative ERC (evolutionary rate covariation) values between genes
    Daniel Himmelstein, Raghavendran Partha
    -ThinkLab (2015-04-22) https://doi.org/10.15363/thinklab.d57

    +ThinkLab (2015-04-22) https://doi.org/f3mqv9
    +DOI: 10.15363/thinklab.d57

    153. Dhimmel/Erc V1.0: Processing Human Evolutionary Rate Covaration Data
    Daniel S. Himmelstein
    -Zenodo (2016-03-28) https://doi.org/10.5281/zenodo.48444

    +Zenodo (2016-03-28) https://doi.org/f3mqwm
    +DOI: 10.5281/zenodo.48444

    154. Creating a catalog of protein interactions
    Daniel Himmelstein, Dexter Hadley, Alexey Strokach
    -ThinkLab (2015-07-01) https://doi.org/10.15363/thinklab.d85

    +ThinkLab (2015-07-01) https://doi.org/f3mqtp
    +DOI: 10.15363/thinklab.d85

    155. Dhimmel/Ppi V1.0: Compiling A Human Protein Interaction Catalog
    Daniel S. Himmelstein, Sergio E. Baranzini
    -Zenodo (2016-03-28) https://doi.org/10.5281/zenodo.48443

    +Zenodo (2016-03-28) https://doi.org/f3mqtw
    +DOI: 10.5281/zenodo.48443

    156. Towards a proteome-scale map of the human protein–protein interaction network
    Jean-François Rual, Kavitha Venkatesan, Tong Hao, Tomoko Hirozane-Kishikawa, Amélie Dricot, Ning Li, Gabriel F. Berriz, Francis D. Gibbons, Matija Dreze, Nono Ayivi-Guedehoussou, … Marc Vidal
    -Nature (2005-09-28) https://doi.org/10.1038/nature04209

    +Nature (2005-09-28) https://doi.org/dw6q23
    +DOI: 10.1038/nature04209 · PMID: 16189514

    157. An empirical framework for binary interactome mapping
    Kavitha Venkatesan, Jean-François Rual, Alexei Vazquez, Ulrich Stelzl, Irma Lemmens, Tomoko Hirozane-Kishikawa, Tong Hao, Martina Zenkner, Xiaofeng Xin, Kwang-Il Goh, … Marc Vidal
    -Nature Methods (2008-12-07) https://doi.org/10.1038/nmeth.1280

    +Nature Methods (2008-12-07) https://doi.org/cn6p3m
    +DOI: 10.1038/nmeth.1280 · PMID: 19060904 · PMCID: PMC2872561

    158. Next-generation sequencing to generate interactome datasets
    Haiyuan Yu, Leah Tardivo, Stanley Tam, Evan Weiner, Fana Gebreab, Changyu Fan, Nenad Svrzikapa, Tomoko Hirozane-Kishikawa, Edward Rietman, Xinping Yang, … Marc Vidal
    -Nature Methods (2011-04-24) https://doi.org/10.1038/nmeth.1597

    +Nature Methods (2011-04-24) https://doi.org/bzrsvs
    +DOI: 10.1038/nmeth.1597 · PMID: 21516116 · PMCID: PMC3188388

    159. A Proteome-Scale Map of the Human Interactome Network
    Thomas Rolland, Murat Taşan, Benoit Charloteaux, Samuel J. Pevzner, Quan Zhong, Nidhi Sahni, Song Yi, Irma Lemmens, Celia Fontanillo, Roberto Mosca, … Marc Vidal
    -Cell (2014-11) https://doi.org/10.1016/j.cell.2014.10.050

    +Cell (2014-11) https://doi.org/f3mn6x
    +DOI: 10.1016/j.cell.2014.10.050 · PMID: 25416956 · PMCID: PMC4266588

    160. Uncovering disease-disease relationships through the incomplete interactome
    J. Menche, A. Sharma, M. Kitsak, S. D. Ghiassian, M. Vidal, J. Loscalzo, A.-L. Barabasi
    -Science (2015-02-19) https://doi.org/10.1126/science.1257601

    +Science (2015-02-19) https://doi.org/f3mn6z
    +DOI: 10.1126/science.1257601 · PMID: 25700523 · PMCID: PMC4435741

    161. The GOA database: Gene Ontology annotation updates for 2015
    -R. P. Huntley, T. Sawford, P. Mutowo-Meullenet, A. Shypitsyna, C. Bonilla, M. J. Martin, C. O’Donovan
    -Nucleic Acids Research (2014-11-06) https://doi.org/10.1093/nar/gku1113

    +Rachael P. Huntley, Tony Sawford, Prudence Mutowo-Meullenet, Aleksandra Shypitsyna, Carlos Bonilla, Maria J. Martin, Claire O’Donovan
    +Nucleic Acids Research (2014-11-06) https://doi.org/35x
    +DOI: 10.1093/nar/gku1113 · PMID: 25378336 · PMCID: PMC4383930

    162. Compiling Gene Ontology annotations into an easy-to-use format
    Daniel Himmelstein, Casey Greene, Venkat Malladi, Frederic Bastian
    -ThinkLab (2015-03-12) https://doi.org/10.15363/thinklab.d39

    +ThinkLab (2015-03-12) https://doi.org/f3mqt9
    +DOI: 10.15363/thinklab.d39

    163. Gene-Ontology: Initial Zenodo Release
    Daniel Himmelstein, Casey Greene, Venkat Malladi, Frederic Bastian, Sergio Baranzini
    -Zenodo (2015-07-28) https://doi.org/10.5281/zenodo.21711

    +Zenodo (2015-07-28) https://doi.org/f3mqvj
    +DOI: 10.5281/zenodo.21711

    164. Precision annotation of digital samples in NCBI’s gene expression omnibus
    Dexter Hadley, James Pan, Osama El-Sayed, Jihad Aljabban, Imad Aljabban, Tej D. Azad, Mohamad O. Hadied, Shuaib Raza, Benjamin Abhishek Rayikanti, Bin Chen, … Atul J. Butte
    -Scientific Data (2017-09-19) https://doi.org/10.1038/sdata.2017.125

    +Scientific Data (2017-09-19) https://doi.org/gbv379
    +DOI: 10.1038/sdata.2017.125 · PMID: 28925997 · PMCID: PMC5604135

    165. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
    R. Edgar
    -Nucleic Acids Research (2002-01-01) https://doi.org/10.1093/nar/30.1.207

    +Nucleic Acids Research (2002-01-01) https://doi.org/fttpkn
    +DOI: 10.1093/nar/30.1.207

    -

    166. NCBI GEO: archive for functional genomics data sets–update
    -T. Barrett, S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, M. Holko, … A. Soboleva
    -Nucleic Acids Research (2012-11-27) https://doi.org/10.1093/nar/gks1193

    +

    166. NCBI GEO: archive for functional genomics data sets—update
    +Tanya Barrett, Stephen E. Wilhite, Pierre Ledoux, Carlos Evangelista, Irene F. Kim, Maxim Tomashevsky, Kimberly A. Marshall, Katherine H. Phillippy, Patti M. Sherman, Michelle Holko, … Alexandra Soboleva
    +Nucleic Acids Research (2012-11-26) https://doi.org/f3mn62
    +DOI: 10.1093/nar/gks1193 · PMID: 23193258 · PMCID: PMC3531084

    167. Dhimmel/Lincs V2.0: Refined Consensus Signatures From Lincs L1000
    Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini
    -Zenodo (2016-03-08) https://doi.org/10.5281/zenodo.47223

    +Zenodo (2016-03-08) https://doi.org/f3mqvr
    +DOI: 10.5281/zenodo.47223

    168. l1000.db: SQLite database of LINCS L1000 metadata
    Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini
    -Figshare (2016) https://doi.org/10.6084/m9.figshare.3085837.v1

    +Figshare (2016) https://doi.org/f3mqtq
    +DOI: 10.6084/m9.figshare.3085837.v1

    169. Assessing the imputation quality of gene expression in LINCS L1000
    Daniel Himmelstein
    -ThinkLab (2016-03-11) https://doi.org/10.15363/thinklab.d185

    +ThinkLab (2016-03-11) https://doi.org/f3mqtr
    +DOI: 10.15363/thinklab.d185

    170. Positive correlations between knockdown and overexpression profiles from LINCS L1000
    Daniel Himmelstein, Casey Greene, Lars Juhl Jensen
    -ThinkLab (2016-02-26) https://doi.org/10.15363/thinklab.d171

    +ThinkLab (2016-02-26) https://doi.org/f3mqt7
    +DOI: 10.15363/thinklab.d171

    171. Announcing PharmacotherapyDB: the Open Catalog of Drug Therapies for Disease
    Daniel Himmelstein
    -ThinkLab (2016-03-15) https://doi.org/10.15363/thinklab.d182

    +ThinkLab (2016-03-15) https://doi.org/f3mqtv
    +DOI: 10.15363/thinklab.d182

    172. PharmacotherapyDB 1.0: the open catalog of drug therapies for disease
    Daniel Himmelstein, Pouya Khankhanian, Christine S. Hessler, Ari J. Green, Sergio Baranzini
    -Figshare (2016) https://doi.org/10.6084/m9.figshare.3103054

    +Figshare (2016) https://doi.org/f3mqvq
    +DOI: 10.6084/m9.figshare.3103054

    173. Dhimmel/Indications V1.0. Pharmacotherapydb: The Open Catalog Of Drug Therapies For Disease
    Daniel S. Himmelstein, Pouya Khankhanian, Christine S. Hessler, Ari J. Green, Sergio E. Baranzini
    -Zenodo (2016-03-15) https://doi.org/10.5281/zenodo.47664

    +Zenodo (2016-03-15) https://doi.org/f3mqwb
    +DOI: 10.5281/zenodo.47664

    174. How should we construct a catalog of drug indications?
    Daniel Himmelstein, Benjamin Good, Tudor Oprea, Allison McCoy, Antoine Lizee
    -ThinkLab (2015-01-13) https://doi.org/10.15363/thinklab.d21

    +ThinkLab (2015-01-13) https://doi.org/f3mqtz
    +DOI: 10.15363/thinklab.d21

    175. Development and evaluation of an ensemble resource linking medications to their indications
    Wei-Qi Wei, Robert M Cronin, Hua Xu, Thomas A Lasko, Lisa Bastarache, Joshua C Denny
    -Journal of the American Medical Informatics Association (2013-09) https://doi.org/10.1136/amiajnl-2012-001431

    +Journal of the American Medical Informatics Association (2013-09) https://doi.org/f3mn63
    +DOI: 10.1136/amiajnl-2012-001431 · PMID: 23576672 · PMCID: PMC3756263

    176. LabeledIn: Cataloging labeled indications for human drugs
    Ritu Khare, Jiao Li, Zhiyong Lu
    -Journal of Biomedical Informatics (2014-12) https://doi.org/10.1016/j.jbi.2014.08.004

    +Journal of Biomedical Informatics (2014-12) https://doi.org/f3mn64
    +DOI: 10.1016/j.jbi.2014.08.004 · PMID: 25220766 · PMCID: PMC4260997

    177. Scaling drug indication curation through crowdsourcing
    -R. Khare, J. D. Burger, J. S. Aberdeen, D. W. Tresner-Kirsch, T. J. Corrales, L. Hirchman, Z. Lu
    -Database (2015-03-22) https://doi.org/10.1093/database/bav016

    +Ritu Khare, John D. Burger, John S. Aberdeen, David W. Tresner-Kirsch, Theodore J. Corrales, Lynette Hirchman, Zhiyong Lu
    +Database (2015-01-01) https://doi.org/f3mn65
    +DOI: 10.1093/database/bav016 · PMID: 25797061 · PMCID: PMC4369375

    178. Processing LabeledIn to extract indications
    Daniel Himmelstein, Ritu Khare
    -ThinkLab (2015-04-02) https://doi.org/10.15363/thinklab.d46

    +ThinkLab (2015-04-02) https://doi.org/f3mqww
    +DOI: 10.15363/thinklab.d46

    179. Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications
    Allison B McCoy, Adam Wright, Archana Laxmisan, Madelene J Ottosen, Jacob A McCoy, David Butten, Dean F Sittig
    -Journal of the American Medical Informatics Association (2012-09) https://doi.org/10.1136/amiajnl-2012-000852

    +Journal of the American Medical Informatics Association (2012-09) https://doi.org/f3mn66
    +DOI: 10.1136/amiajnl-2012-000852 · PMID: 22582202 · PMCID: PMC3422843

    180. Extracting indications from the ehrlink resource
    Daniel Himmelstein
    -ThinkLab (2015-05-01) https://doi.org/10.15363/thinklab.d62

    +ThinkLab (2015-05-01) https://doi.org/f3mqwv
    +DOI: 10.15363/thinklab.d62

    181. Expert curation of our indication catalog for disease-modifying treatments
    Daniel Himmelstein, Pouya Khankhanian, Chrissy Hessler
    -ThinkLab (2015-07-14) https://doi.org/10.15363/thinklab.d95

    +ThinkLab (2015-07-14) https://doi.org/f3mqwn
    +DOI: 10.15363/thinklab.d95

    182. Enabling reproducibility and reuse
    Jesse Spaulding, Daniel Himmelstein, Casey Greene, Benjamin Good
    -ThinkLab (2015-01-16) https://doi.org/10.15363/thinklab.d23

    +ThinkLab (2015-01-16) https://doi.org/f3mn67
    +DOI: 10.15363/thinklab.d23

    183. The need and drive for open data in biomedical publishing
    Iain Hrynaszkiewicz
    -Serials: The Journal for the Serials Community (2011-03-01) https://doi.org/10.1629/2431

    +Serials: The Journal for the Serials Community (2011-03-01) https://doi.org/c7zvmd
    +DOI: 10.1629/2431

    184. The Open Knowledge Foundation: Open Data Means Better Science
    Jennifer C. Molloy
    -PLoS Biology (2011-12-06) https://doi.org/10.1371/journal.pbio.1001195

    +PLoS Biology (2011-12-06) https://doi.org/g3b
    +DOI: 10.1371/journal.pbio.1001195 · PMID: 22162946 · PMCID: PMC3232214

    185. How open science helps researchers succeed
    Erin C McKiernan, Philip E Bourne, C Titus Brown, Stuart Buck, Amye Kenall, Jennifer Lin, Damon McDougall, Brian A Nosek, Karthik Ram, Courtney K Soderberg, … Tal Yarkoni
    -eLife (2016-07-07) https://doi.org/10.7554/elife.16800

    +eLife (2016-07-07) https://doi.org/gbqsng
    +DOI: 10.7554/elife.16800 · PMID: 27387362 · PMCID: PMC4973366

    186. Data reuse and the open data citation advantage
    Heather A. Piwowar, Todd J. Vision
    -PeerJ (2013-10-01) https://doi.org/10.7717/peerj.175

    +PeerJ (2013-10-01) https://doi.org/f3mn68
    +DOI: 10.7717/peerj.175 · PMID: 24109559 · PMCID: PMC3792178

    187. Enhancing reproducibility for computational methods
    V. Stodden, M. McNutt, D. H. Bailey, E. Deelman, Y. Gil, B. Hanson, M. A. Heroux, J. P. A. Ioannidis, M. Taufer
    -Science (2016-12-08) https://doi.org/10.1126/science.aah6168

    +Science (2016-12-08) https://doi.org/gbr42b
    +DOI: 10.1126/science.aah6168 · PMID: 27940837

    188. Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research
    Victoria Stodden, Sheila Miguez
    -Journal of Open Research Software (2014-07-09) https://doi.org/10.5334/jors.ay

    +Journal of Open Research Software (2014-07-09) https://doi.org/f3mn69
    +DOI: 10.5334/jors.ay

    189. Disclose all data in publications
    Keith Baggerly
    -Nature (2010-09-23) https://doi.org/10.1038/467401b

    +Nature (2010-09) https://doi.org/fhc9z5
    +DOI: 10.1038/467401b · PMID: 20864982

    190. Open by default: a proposed copyright license and waiver agreement for open access research and data in peer-reviewed journals
    Iain Hrynaszkiewicz, Matthew J Cockerill
    -BMC Research Notes (2012) https://doi.org/10.1186/1756-0500-5-494

    +BMC Research Notes (2012-09-07) https://doi.org/f3mn7c
    +DOI: 10.1186/1756-0500-5-494 · PMID: 22958225 · PMCID: PMC3465200

    191. Creative Commons licenses and the non-commercial condition: Implications for the re-use of biodiversity information
    Gregor Hagedorn, Daniel Mietchen, Robert Morris, Donat Agosti, Lyubomir Penev, Walter Berendsohn, Donald Hobern
    -ZooKeys (2011-11-28) https://doi.org/10.3897/zookeys.150.2189

    +ZooKeys (2011-11-28) https://doi.org/g25
    +DOI: 10.3897/zookeys.150.2189 · PMID: 22207810 · PMCID: PMC3234435

    192. One network to rule them all
    Daniel Himmelstein, Lars Juhl Jensen
    -ThinkLab (2015-08-14) https://doi.org/10.15363/thinklab.d102

    +ThinkLab (2015-08-14) https://doi.org/f3mqt4
    +DOI: 10.15363/thinklab.d102

    193. Integrating resources with disparate licensing into an open network
    Daniel Himmelstein, Lars Juhl Jensen, MacKenzie Smith, Katie Fortney, Caty Chung
    -ThinkLab (2015-08-28) https://doi.org/10.15363/thinklab.d107

    +ThinkLab (2015-08-28) https://doi.org/bfmk
    +DOI: 10.15363/thinklab.d107

    194. Legal confusion threatens to slow data science
    Simon Oxenham
    -Nature (2016-08-03) https://doi.org/10.1038/536016a

    +Nature (2016-08-03) https://doi.org/bndt
    +DOI: 10.1038/536016a · PMID: 27488781

    195. LINCS L1000 licensing
    Daniel Himmelstein
    -ThinkLab (2015-09-28) https://doi.org/10.15363/thinklab.d110

    +ThinkLab (2015-09-28) https://doi.org/bfmn
    +DOI: 10.15363/thinklab.d110

    196. Sounding the alarm on DrugBank’s new license and terms of use
    Daniel Himmelstein, Katie Fortney, Craig Knox, Christopher Southan
    -ThinkLab (2016-05-08) https://doi.org/10.15363/thinklab.d213

    +ThinkLab (2016-05-08) https://doi.org/bgnh
    +DOI: 10.15363/thinklab.d213

    197. Incomplete Interactome licensing
    Daniel Himmelstein
    -ThinkLab (2015-10-01) https://doi.org/10.15363/thinklab.d111

    +ThinkLab (2015-10-01) https://doi.org/bfmp
    +DOI: 10.15363/thinklab.d111

    198. Who owns scientific data? The impact of intellectual property rights on the scientific publication chain
    Roger Elliott
    -Learned Publishing (2005-04) https://doi.org/10.1087/0953151053584984

    +Learned Publishing (2005-04) https://doi.org/cxfd27
    +DOI: 10.1087/0953151053584984

    199. MSigDB licensing
    Daniel Himmelstein
    -ThinkLab (2015-09-28) https://doi.org/10.15363/thinklab.d108

    +ThinkLab (2015-09-28) https://doi.org/bfmm
    +DOI: 10.15363/thinklab.d108

    200. Molecular signatures database (MSigDB) 3.0
    A. Liberzon, A. Subramanian, R. Pinchback, H. Thorvaldsdottir, P. Tamayo, J. P. Mesirov
    -Bioinformatics (2011-05-05) https://doi.org/10.1093/bioinformatics/btr260

    +Bioinformatics (2011-05-05) https://doi.org/b8mx73
    +DOI: 10.1093/bioinformatics/btr260 · PMID: 21546393 · PMCID: PMC3106198

    201. Assessing the effectiveness of our hetnet permutations
    Daniel Himmelstein
    -ThinkLab (2016-02-25) https://doi.org/10.15363/thinklab.d178

    +ThinkLab (2016-02-25) https://doi.org/f3mqt5
    +DOI: 10.15363/thinklab.d178

    202. Randomization Techniques for Graphs
    Sami Hanhijärvi, Gemma C. Garriga, Kai Puolamäki
    -Proceedings of the 2009 SIAM International Conference on Data Mining (2009-04-30) https://doi.org/10.1137/1.9781611972795.67

    +Proceedings of the 2009 SIAM International Conference on Data Mining (2009-04-30) https://doi.org/f3mn58
    +DOI: 10.1137/1.9781611972795.67

    203. Permuting hetnets and implementing randomized edge swaps in cypher
    Daniel Himmelstein
    -ThinkLab (2015-12-21) https://doi.org/10.15363/thinklab.d136

    +ThinkLab (2015-12-21) https://doi.org/f3mqt6
    +DOI: 10.15363/thinklab.d136

    204. Use of Graph Database for the Integration of Heterogeneous Biological Data
    Byoung-Ha Yoon, Seon-Kyu Kim, Seon-Young Kim
    -Genomics & Informatics (2017) https://doi.org/10.5808/gi.2017.15.1.19

    +Genomics & Informatics (2017) https://doi.org/f93xct
    +DOI: 10.5808/gi.2017.15.1.19 · PMID: 28416946 · PMCID: PMC5389944

    205. Comparative analysis of Relational and Graph databases
    Garima Jaiswal
    -IOSR Journal of Engineering (2013-08) https://doi.org/10.9790/3021-03822527

    +IOSR Journal of Engineering (2013-08) https://doi.org/gbr42z
    +DOI: 10.9790/3021-03822527

    206. Are graph databases ready for bioinformatics?
    -C. T. Have, L. J. Jensen
    -Bioinformatics (2013-10-17) https://doi.org/10.1093/bioinformatics/btt549

    +Christian Theil Have, Lars Juhl Jensen
    +Bioinformatics (2013-10-17) https://doi.org/f3mn4w
    +DOI: 10.1093/bioinformatics/btt549 · PMID: 24135261 · PMCID: PMC3842757

    207. Representing and querying disease networks using graph databases
    Artem Lysenko, Irina A. Roznovăţ, Mansoor Saqi, Alexander Mazein, Christopher J Rawlings, Charles Auffray
    -BioData Mining (2016-07-25) https://doi.org/10.1186/s13040-016-0102-8

    +BioData Mining (2016-07-25) https://doi.org/gbr42v
    +DOI: 10.1186/s13040-016-0102-8 · PMID: 27462371 · PMCID: PMC4960687

    208. Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks
    Irina Balaur, Alexander Mazein, Mansoor Saqi, Artem Lysenko, Christopher J. Rawlings, Charles Auffray
    -Bioinformatics (2016-12-19) https://doi.org/10.1093/bioinformatics/btw731

    +Bioinformatics (2016-12-19) https://doi.org/f9kpsz
    +DOI: 10.1093/bioinformatics/btw731 · PMID: 27993779 · PMCID: PMC5408918

    209. The Network Library: a framework to rapidly integrate network biology resources
    Georg Summer, Thomas Kelder, Marijana Radonjic, Marc van Bilsen, Suzan Wopereis, Stephane Heymans
    -Bioinformatics (2016-09-01) https://doi.org/10.1093/bioinformatics/btw436

    +Bioinformatics (2016-09-01) https://doi.org/f86d74
    +DOI: 10.1093/bioinformatics/btw436 · PMID: 27587664

    210. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
    Christopher J. Mungall, Julie A. McMurry, Sebastian Köhler, James P. Balhoff, Charles Borromeo, Matthew Brush, Seth Carbon, Tom Conlin, Nathan Dunn, Mark Engelstad, … Melissa A. Haendel
    -Nucleic Acids Research (2016-11-29) https://doi.org/10.1093/nar/gkw1128

    +Nucleic Acids Research (2016-11-29) https://doi.org/f9v7bz
    +DOI: 10.1093/nar/gkw1128 · PMID: 27899636 · PMCID: PMC5210586

    211. Using the neo4j graph database for hetnets
    Daniel Himmelstein
    -ThinkLab (2015-10-02) https://doi.org/10.15363/thinklab.d112

    +ThinkLab (2015-10-02) https://doi.org/f3mqvk
    +DOI: 10.15363/thinklab.d112

    -

    212. dhimmel/hetio v0.2.0: Neo4j export, Cypher query creation, hetnet stats, and other enhancements
    +

    212. Dhimmel/Hetio V0.2.0: Neo4J Export, Cypher Query Creation, Hetnet Stats, And Other Enhancements
    Daniel Himmelstein
    -Zenodo (2016-09-05) https://doi.org/10.5281/zenodo.61571

    +Zenodo (2016-09-05) https://doi.org/gbr42q
    +DOI: 10.5281/zenodo.61571

    213. Hosting Hetionet in the cloud: creating a public Neo4j instance
    Daniel Himmelstein
    -ThinkLab (2016-06-23) https://doi.org/10.15363/thinklab.d216

    +ThinkLab (2016-06-23) https://doi.org/brsc
    +DOI: 10.15363/thinklab.d216

    214. Bioboxes: standardised containers for interchangeable bioinformatics software
    Peter Belmann, Johannes Dröge, Andreas Bremges, Alice C. McHardy, Alexander Sczyrba, Michael D. Barton
    -GigaScience (2015-10-15) https://doi.org/10.1186/s13742-015-0087-0

    +GigaScience (2015-10-15) https://doi.org/gbr42d
    +DOI: 10.1186/s13742-015-0087-0 · PMID: 26473029 · PMCID: PMC4607242

    215. Reproducibility of computational workflows is automated using continuous analysis
    Brett K Beaulieu-Jones, Casey S Greene
    -Nature Biotechnology (2017-03-13) https://doi.org/10.1038/nbt.3780

    +Nature Biotechnology (2017-03-13) https://doi.org/f9ttx6
    +DOI: 10.1038/nbt.3780 · PMID: 28288103 · PMCID: PMC6103790

    216. Estimating the complexity of hetnet traversal
    Daniel Himmelstein, Antoine Lizee
    -ThinkLab (2016-03-22) https://doi.org/10.15363/thinklab.d187

    +ThinkLab (2016-03-22) https://doi.org/gbr42x
    +DOI: 10.15363/thinklab.d187

    217. Alternative Transformations to Handle Extreme Values of the Dependent Variable
    John B. Burbidge, Lonnie Magee, A. Leslie Robb
    -Journal of the American Statistical Association (1988-03) https://doi.org/10.2307/2288929

    +Journal of the American Statistical Association (1988-03) https://doi.org/bggvmg
    +DOI: 10.1080/01621459.1988.10478575

    218. Transforming DWPCs for hetnet edge prediction
    Daniel Himmelstein, Pouya Khankhanian, Antoine Lizee
    -ThinkLab (2016-04-01) https://doi.org/10.15363/thinklab.d193

    +ThinkLab (2016-04-01) https://doi.org/f3qbmd
    +DOI: 10.15363/thinklab.d193

    219. Assessing the informativeness of features
    Daniel Himmelstein
    -ThinkLab (2015-10-04) https://doi.org/10.15363/thinklab.d115

    +ThinkLab (2015-10-04) https://doi.org/f3qbmb
    +DOI: 10.15363/thinklab.d115

    220. Edge dropout contamination in hetnet edge prediction
    Daniel Himmelstein
    -ThinkLab (2016-05-16) https://doi.org/10.15363/thinklab.d215

    +ThinkLab (2016-05-16) https://doi.org/f3qbmm
    +DOI: 10.15363/thinklab.d215

    221. Decomposing predictions into their network support
    Daniel Himmelstein
    -ThinkLab (2016-12-21) https://doi.org/10.15363/thinklab.d229

    +ThinkLab (2016-12-21) https://doi.org/gbr42j
    +DOI: 10.15363/thinklab.d229

    222. Decomposing the DWPC to assess intermediate node or edge contributions
    Daniel Himmelstein
    -ThinkLab (2016-12-15) https://doi.org/10.15363/thinklab.d228

    +ThinkLab (2016-12-15) https://doi.org/gbr42h
    +DOI: 10.15363/thinklab.d228

    223. Network Edge Prediction: Estimating the prior
    Antoine Lizee, Daniel Himmelstein
    -ThinkLab (2016-04-14) https://doi.org/10.15363/thinklab.d201

    +ThinkLab (2016-04-14) https://doi.org/f3qbmg
    +DOI: 10.15363/thinklab.d201

    224. Network Edge Prediction: how to deal with self-testing
    Antoine Lizee, Daniel Himmelstein
    -ThinkLab (2016-04-05) https://doi.org/10.15363/thinklab.d194

    +ThinkLab (2016-04-05) https://doi.org/f3qbmf
    +DOI: 10.15363/thinklab.d194

    225. Cataloging drug–disease therapies in the ClinicalTrials.gov database
    Daniel Himmelstein
    -ThinkLab (2016-05-08) https://doi.org/10.15363/thinklab.d212

    +ThinkLab (2016-05-08) https://doi.org/f3qbmk
    +DOI: 10.15363/thinklab.d212

    226. A standard database for drug repositioning
    Adam S. Brown, Chirag J. Patel
    -Scientific Data (2017-03-14) https://doi.org/10.1038/sdata.2017.29

    +Scientific Data (2017-03-14) https://doi.org/gbr42s
    +DOI: 10.1038/sdata.2017.29 · PMID: 28291243 · PMCID: PMC5349249

    227. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning
    -Khader Shameer, Benjamin S. Glicksberg, Rachel Hodos, Kipp W. Johnson, Marcus A. Badgeley, Ben Readhead, Max S. Tomlinson, Timothy O’Connor, Riccardo Miotto, Brian A. Kidd, … Joel T. Dudley
    -Briefings in Bioinformatics (2017-02-15) https://doi.org/10.1093/bib/bbw136

    +Khader Shameer, Benjamin S Glicksberg, Rachel Hodos, Kipp W Johnson, Marcus A Badgeley, Ben Readhead, Max S Tomlinson, Timothy O’Connor, Riccardo Miotto, Brian A Kidd, … Joel T Dudley
    +Briefings in Bioinformatics (2017-02-15) https://doi.org/gbr42t
    +DOI: 10.1093/bib/bbw136 · PMID: 28200013 · PMCID: PMC6192146

    228. Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources
    Mark E Sharp
    -Journal of Biomedical Semantics (2017-01-10) https://doi.org/10.1186/s13326-016-0110-0

    +Journal of Biomedical Semantics (2017-01-10) https://doi.org/gbr42w
    +DOI: 10.1186/s13326-016-0110-0 · PMID: 28069052 · PMCID: PMC5223332

    229. Rephetio: Repurposing drugs on a hetnet [proposal]
    Daniel Himmelstein, Antoine Lizee, Chrissy Hessler, Leo Brueggeman, Sabrina Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio Baranzini
    -ThinkLab (2015-01-12) https://doi.org/10.15363/thinklab.a5

    +ThinkLab (2015-01-12) https://doi.org/bd9g
    +DOI: 10.15363/thinklab.a5

    230. Measuring user contribution and content creation
    Daniel Himmelstein, Antoine Lizee
    -ThinkLab (2016-04-11) https://doi.org/10.15363/thinklab.d200

    +ThinkLab (2016-04-11) https://doi.org/f3mqvn
    +DOI: 10.15363/thinklab.d200

    231. This revolution will be digitized: online tools for radical collaboration
    C. Patil, V. Siegel
    -Disease Models & Mechanisms (2009-04-30) https://doi.org/10.1242/dmm.003285

    +Disease Models & Mechanisms (2009-04-30) https://doi.org/fvjhcj
    +DOI: 10.1242/dmm.003285 · PMID: 19407323 · PMCID: PMC2675795

    232. Publishing the research process
    Daniel Mietchen, Ross Mounce, Lyubomir Penev
    -Research Ideas and Outcomes (2015-12-17) https://doi.org/10.3897/rio.1.e7547

    +Research Ideas and Outcomes (2015-12-17) https://doi.org/f3mn7d
    +DOI: 10.3897/rio.1.e7547

    233. Does it take too long to publish research?
    Kendall Powell
    -Nature (2016-02-10) https://doi.org/10.1038/530148a

    +Nature (2016-02-10) https://doi.org/f3mn4t
    +DOI: 10.1038/530148a · PMID: 26863966

    234. Accelerating scientific publication in biology
    Ronald D. Vale
    -Proceedings of the National Academy of Sciences (2015-10-27) https://doi.org/10.1073/pnas.1511912112

    +Proceedings of the National Academy of Sciences (2015-10-27) https://doi.org/f3mn7f
    +DOI: 10.1073/pnas.1511912112 · PMID: 26508643 · PMCID: PMC4640799

    235. Reproducibility: A tragedy of errors
    David B. Allison, Andrew W. Brown, Brandon J. George, Kathryn A. Kaiser
    -Nature (2016-02-03) https://doi.org/10.1038/530027a

    +Nature (2016-02-03) https://doi.org/f3mn7g
    +DOI: 10.1038/530027a · PMID: 26842041 · PMCID: PMC4831566

    236. Workshop to analyze LINCS data for the Systems Pharmacology course at UCSF
    Daniel Himmelstein, Kathleen Keough, Misha Vysotskiy, Jeffrey Kim, Beau Norgeot, Julia Cluceru, Marjorie Imperial, Emmalyn Chen, Jasleen Sodhi, Elizabeth Levy
    -ThinkLab (2016-03-08) https://doi.org/10.15363/thinklab.d181

    +ThinkLab (2016-03-08) https://doi.org/f3mn57
    +DOI: 10.15363/thinklab.d181

    237. Why we are teaching science wrong, and how to make it right
    M. Mitchell Waldrop
    -Nature (2015-07-15) https://doi.org/10.1038/523272a

    +Nature (2015-07-15) https://doi.org/f3mn7h
    +DOI: 10.1038/523272a · PMID: 26178948

    238. Going paperless: The digital lab
    Jim Giles
    -Nature (2012-01-25) https://doi.org/10.1038/481430a

    +Nature (2012-01-25) https://doi.org/fznpgr
    +DOI: 10.1038/481430a · PMID: 22281576

    239. Systematic integration of biomedical knowledge prioritizes drugs for repurposing
    Daniel S. Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L. Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E. Baranzini
    -Cold Spring Harbor Laboratory (2016-11-14) https://doi.org/10.1101/087619

    +Cold Spring Harbor Laboratory (2016-11-14) https://doi.org/bs4f
    +DOI: 10.1101/087619

    240. Rephetio: Repurposing drugs on a hetnet [report]
    Daniel Himmelstein, Antoine Lizee, Chrissy Hessler, Leo Brueggeman, Sabrina Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio Baranzini
    -ThinkLab (2016-11-13) https://doi.org/10.15363/thinklab.a7

    +ThinkLab (2016-11-13) https://doi.org/bszr
    +DOI: 10.15363/thinklab.a7

    241. Figshare depositions from Project Rephetio
    Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini
    -Figshare (2017) https://doi.org/10.6084/m9.figshare.c.2861359.v1

    +Figshare (2017) https://doi.org/ccq3
    +DOI: 10.6084/m9.figshare.c.2861359.v1

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/manuscript.md b/manuscript.md index 2205734..1d966b3 100644 --- a/manuscript.md +++ b/manuscript.md @@ -9,7 +9,7 @@ author-meta: - Ari Green - "Pouya\_Khankhanian" - "Sergio\_E.\_Baranzini" -date-meta: '2017-10-03' +date-meta: '2019-03-14' keywords: - Rephetio - Hetionet @@ -25,10 +25,14 @@ title: Systematic integration of biomedical knowledge prioritizes drugs for repu + + -This manuscript was automatically generated -from [dhimmel/rephetio-manuscript@7b5b0e6](https://github.com/dhimmel/rephetio-manuscript/tree/7b5b0e67fb9ab159c84c230dd5c07001941d82af) -on October 3, 2017. +This manuscript +([permalink](https://dhimmel.github.io/rephetio-manuscript/v/1ddd48dabdb669a3c09b1a3dfdfee455f642b5a9/)) +was automatically generated +from [dhimmel/rephetio-manuscript@1ddd48d](https://github.com/dhimmel/rephetio-manuscript/tree/1ddd48dabdb669a3c09b1a3dfdfee455f642b5a9) +on March 14, 2019. ## Authors @@ -36,22 +40,22 @@ on October 3, 2017. + **Daniel S. Himmelstein**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0002-3012-7446](https://orcid.org/0000-0002-3012-7446) - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [dhimmel](https://github.com/dhimmel) - · ![Twitter icon](images/twitter.svg){height="13px"} + · ![Twitter icon](images/twitter.svg){.inline_icon} [dhimmel](https://twitter.com/dhimmel)
    Program in Biological & Medical Informatics, University of California, San Francisco; Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania + **Antoine Lizee**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0002-1073-3190](https://orcid.org/0000-0002-1073-3190) - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [antoine-lizee](https://github.com/antoine-lizee) - · ![Twitter icon](images/twitter.svg){height="13px"} + · ![Twitter icon](images/twitter.svg){.inline_icon} [A_Lizee](https://twitter.com/A_Lizee)
    Department of Neurology, University of California, San Francisco; ITUN-CRTI-UMR 1064 Inserm, University of Nantes @@ -63,52 +67,52 @@ on October 3, 2017. + **Leo Brueggeman**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0002-3586-3442](https://orcid.org/0000-0002-3586-3442) - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [LABrueggs](https://github.com/LABrueggs) - · ![Twitter icon](images/twitter.svg){height="13px"} + · ![Twitter icon](images/twitter.svg){.inline_icon} [LeoBman](https://twitter.com/LeoBman)
    Department of Neurology, University of California, San Francisco; University of Iowa + ** Sabrina L. Chen**
    - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [sabrinalchen](https://github.com/sabrinalchen)
    Department of Neurology, University of California, San Francisco; Johns Hopkins University + ** Dexter Hadley**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0003-0990-4674](https://orcid.org/0000-0003-0990-4674) - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [idrdex](https://github.com/idrdex) - · ![Twitter icon](images/twitter.svg){height="13px"} + · ![Twitter icon](images/twitter.svg){.inline_icon} [iDrDex](https://twitter.com/iDrDex)
    Institute for Computational Health Sciences, Department of Pediatrics; University of California, San Francisco + **Ari Green**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0001-9275-3066](https://orcid.org/0000-0001-9275-3066)
    Department of Neurology, University of California, San Francisco + **Pouya Khankhanian**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0001-8075-4176](https://orcid.org/0000-0001-8075-4176)
    Department of Neurology, University of California, San Francisco; Center for Neuroengineering and Therapeutics, University of Pennsylvania + **Sergio E. Baranzini**
    - ![ORCID icon](images/orcid.svg){height="13px"} + ![ORCID icon](images/orcid.svg){.inline_icon} [0000-0003-0067-194X](https://orcid.org/0000-0003-0067-194X) - · ![GitHub icon](images/github.svg){height="13px"} + · ![GitHub icon](images/github.svg){.inline_icon} [sebaran](https://github.com/sebaran)
    Department of Neurology, University of California, San Francisco diff --git a/manuscript.pdf b/manuscript.pdf index 1cc906b..25178ba 100644 Binary files a/manuscript.pdf and b/manuscript.pdf differ diff --git a/references.json b/references.json index 78fbe22..0a288dd 100644 --- a/references.json +++ b/references.json @@ -1,92 +1,28 @@ [ { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 31 - ] - ], - "date-time": "2017-08-31T13:42:10Z", - "timestamp": 1504186930208 - }, - "reference-count": 0, "publisher": "American Medical Association (AMA)", "issue": "8", - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, - "published-print": { - "date-parts": [ - [ - 2004, - 8, - 1 - ] - ] - }, "DOI": "10.1001/archneur.61.8.1254", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2004, - 8, - 16 - ] - ], - "date-time": "2004-08-16T20:33:39Z", - "timestamp": 1092688419000 - }, "source": "Crossref", - "is-referenced-by-count": 108, "title": "Treatment of Refractory Status Epilepticus With Inhalational Anesthetic Agents Isoflurane and Desflurane", - "prefix": "10.1001", "volume": "61", "author": [ { "given": "Seyed M.", - "family": "Mirsattari", - "affiliation": [] + "family": "Mirsattari" }, { "given": "Michael D.", - "family": "Sharpe", - "affiliation": [] + "family": "Sharpe" }, { "given": "G. 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"relation": { - "cites": [] - }, - "subject": [ - "Animal Science and Zoology", - "Ecology, Evolution, Behavior and Systematics" - ], + "URL": "https://doi.org/g25", "container-title-short": "ZK", - "short_url": "https://doi.org/g25", + "PMCID": "PMC3234435", + "PMID": "22207810", "id": "8hTZ1RWy" }, { @@ -25469,12 +9444,18 @@ "title": "Anatomical Therapeutic Chemical Classification System (WHO)", "container-title": "The SAGE Encyclopedia of Pharmacology and Society", "URL": "https://doi.org/10.4135/9781483349985.n37", - "short_url": "https://doi.org/gbr42m", "id": "aIzBdEjd" }, { "type": "article", "id": "1yPa6qxQ", + "categories": [ + "Gene Ontology", + "Annotations", + "Ontologies", + "Propagation", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25510,12 +9491,19 @@ "DOI": "10.5281/zenodo.21711", "publisher": "Zenodo", "title": "Gene-Ontology: Initial Zenodo Release", - "URL": "https://doi.org/10.5281/zenodo.21711", - "short_url": "https://doi.org/f3mqvj" + "URL": "https://doi.org/f3mqvj" }, { "type": "article", "id": "kVUF2wf3", + "categories": [ + "hetionet", + "rephetio", + "hetnet", + "network", + "drug repurposing", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25535,12 +9523,18 @@ "DOI": "10.5281/zenodo.268568", "publisher": "Zenodo", "title": "Dhimmel/Hetionet V1.0.0: Hetionet V1.0 In Json, Tsv, And Neo4J Formats", - "URL": "https://doi.org/10.5281/zenodo.268568", - "short_url": "https://doi.org/gbr42n" + "URL": "https://doi.org/gbr42n" }, { "type": "article", "id": "YJJU5X2C", + "categories": [ + "rephetio", + "machine learning", + "hetnet edge prediction", + "drug repurposing", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25560,12 +9554,15 @@ "DOI": "10.5281/zenodo.268654", "publisher": "Zenodo", "title": "Dhimmel/Learn V1.0: The Machine Learning Repository For Project Rephetio", - "URL": "https://doi.org/10.5281/zenodo.268654", - "short_url": "https://doi.org/gbr42p" + "URL": "https://doi.org/gbr42p" }, { "type": "article", "id": "utu7TxwB", + "categories": [ + "TISSUES", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25589,12 +9586,15 @@ "DOI": "10.5281/zenodo.27244", "publisher": "Zenodo", "title": "Gene–Tissue Relationships From The Tissues Database", - "URL": "https://doi.org/10.5281/zenodo.27244", - "short_url": "https://doi.org/f3mqv8" + "URL": "https://doi.org/f3mqv8" }, { "type": "article", "id": "3tDTWGId", + "categories": [ + "BindingDB", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25622,12 +9622,18 @@ "DOI": "10.5281/zenodo.33987", "publisher": "Zenodo", "title": "Processing The October 2015 Bindingdb", - "URL": "https://doi.org/10.5281/zenodo.33987", - "short_url": "https://doi.org/f3mqvp" + "URL": "https://doi.org/f3mqvp" }, { "type": "article", "id": "bqoSmLv6", + "categories": [ + "SIDER 4", + "SIDER", + "side effects", + "drug labels", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25647,12 +9653,15 @@ "DOI": "10.5281/zenodo.45521", "publisher": "Zenodo", "title": "Extracting Tidy And User-Friendly Tsvs From Sider 4.1", - "URL": "https://doi.org/10.5281/zenodo.45521", - "short_url": "https://doi.org/f3mqwd" + "URL": "https://doi.org/f3mqwd" }, { "type": "article", "id": "CeE6BNL6", + "categories": [ + "Entrez Gene", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25672,12 +9681,18 @@ "DOI": "10.5281/zenodo.45524", "publisher": "Zenodo", "title": "Processed Entrez Gene Datasets For Humans V1.0", - "URL": "https://doi.org/10.5281/zenodo.45524", - "short_url": "https://doi.org/f3mqvz" + "URL": "https://doi.org/f3mqvz" }, { "type": "article", "id": "1CvIurqPN", + "categories": [ + "Uberon", + "Anatomical structures", + "Homo sapiens", + "Ontology", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25697,12 +9712,17 @@ "DOI": "10.5281/zenodo.45527", "publisher": "Zenodo", "title": "User-Friendly Anatomical Structures Data From The Uberon Ontology V1.0", - "URL": "https://doi.org/10.5281/zenodo.45527", - "short_url": "https://doi.org/f3mqtt" + "URL": "https://doi.org/f3mqtt" }, { "type": "article", "id": "AoxkilGE", + "categories": [ + "DrugBank", + "Compounds", + "Drugs", + "Chemical Similarity" + ], "author": [ { "family": "Himmelstein", @@ -25722,12 +9742,19 @@ "DOI": "10.5281/zenodo.45579", "publisher": "Zenodo", "title": "User-Friendly Extensions Of The Drugbank Database V1.0", - "URL": "https://doi.org/10.5281/zenodo.45579", - "short_url": "https://doi.org/f3mqwk" + "URL": "https://doi.org/f3mqwk" }, { "type": "article", "id": "rV9FuvXN", + "categories": [ + "Disease Ontology", + "DO", + "DO Slim", + "Ontology", + "Disease", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25747,12 +9774,17 @@ "DOI": "10.5281/zenodo.45584", "publisher": "Zenodo", "title": "User-Friendly Extensions To The Disease Ontology V1.0", - "URL": "https://doi.org/10.5281/zenodo.45584", - "short_url": "https://doi.org/f3mqvc" + "URL": "https://doi.org/f3mqvc" }, { "type": "article", "id": "nO29zy5i", + "categories": [ + "MeSH", + "Medical Subject Headings", + "Symptoms", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25772,12 +9804,17 @@ "DOI": "10.5281/zenodo.45586", "publisher": "Zenodo", "title": "User-Friendly Extensions To Mesh V1.0", - "URL": "https://doi.org/10.5281/zenodo.45586", - "short_url": "https://doi.org/f3mqvt" + "URL": "https://doi.org/f3mqvt" }, { "type": "article", "id": "164R9SolI", + "categories": [ + "STARGEO", + "gene expression", + "disease", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25805,12 +9842,18 @@ "DOI": "10.5281/zenodo.46866", "publisher": "Zenodo", "title": "Dhimmel/Stargeo V1.0: Differentially Expressed Genes For 48 Diseases From Stargeo", - "URL": "https://doi.org/10.5281/zenodo.46866", - "short_url": "https://doi.org/f3mqvg" + "URL": "https://doi.org/f3mqvg" }, { "type": "article", "id": "ZQHbR2c0", + "categories": [ + "Bgee", + "expression", + "transcriptome", + "data integration", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25838,12 +9881,19 @@ "DOI": "10.5281/zenodo.47157", "publisher": "Zenodo", "title": "Dhimmel/Bgee V1.0: Anatomy-Specific Gene Expression In Humans From Bgee", - "URL": "https://doi.org/10.5281/zenodo.47157", - "short_url": "https://doi.org/f3mqv2" + "URL": "https://doi.org/f3mqv2" }, { "type": "article", "id": "1DJZvtwP1", + "categories": [ + "LINCS", + "L1000", + "consensus", + "database", + "perturbation", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25871,12 +9921,19 @@ "DOI": "10.5281/zenodo.47223", "publisher": "Zenodo", "title": "Dhimmel/Lincs V2.0: Refined Consensus Signatures From Lincs L1000", - "URL": "https://doi.org/10.5281/zenodo.47223", - "short_url": "https://doi.org/f3mqvr" + "URL": "https://doi.org/f3mqvr" }, { "type": "article", "id": "10KA5jTBQ", + "categories": [ + "indications", + "PharmacotherapyDB", + "disease modifying", + "drugs", + "disease", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25911,12 +9968,16 @@ "DOI": "10.5281/zenodo.47664", "publisher": "Zenodo", "title": "Dhimmel/Indications V1.0. Pharmacotherapydb: The Open Catalog Of Drug Therapies For Disease", - "URL": "https://doi.org/10.5281/zenodo.47664", - "short_url": "https://doi.org/f3mqwb" + "URL": "https://doi.org/f3mqwb" }, { "type": "article", "id": "unrqUImI", + "categories": [ + "DISEASES", + "association", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25940,12 +10001,17 @@ "DOI": "10.5281/zenodo.48425", "publisher": "Zenodo", "title": "Dhimmel/Diseases V1.0: Processing The Diseases Database Of Gene–Disease Associations", - "URL": "https://doi.org/10.5281/zenodo.48425", - "short_url": "https://doi.org/f3mqv7" + "URL": "https://doi.org/f3mqv7" }, { "type": "article", "id": "RBCqf70A", + "categories": [ + "DisGeNET", + "association", + "disease", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25969,12 +10035,18 @@ "DOI": "10.5281/zenodo.48426", "publisher": "Zenodo", "title": "Dhimmel/Disgenet V1.0: Processing The Disgenet Database Of Gene–Disease Associations", - "URL": "https://doi.org/10.5281/zenodo.48426", - "short_url": "https://doi.org/f3mqwt" + "URL": "https://doi.org/f3mqwt" }, { "type": "article", "id": "1EmRGtMf5", + "categories": [ + "DOAF", + "association", + "disease", + "gene", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -25994,12 +10066,19 @@ "DOI": "10.5281/zenodo.48427", "publisher": "Zenodo", "title": "Dhimmel/Doaf V1.0: Processing The Doaf Database Of Gene–Disease Associations", - "URL": "https://doi.org/10.5281/zenodo.48427", - "short_url": "https://doi.org/f3mqwx" + "URL": "https://doi.org/f3mqwx" }, { "type": "article", "id": "Iq5Pnl3b", + "categories": [ + "GWAS Catalog", + "loci", + "association", + "genes", + "disease", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26023,12 +10102,17 @@ "DOI": "10.5281/zenodo.48428", "publisher": "Zenodo", "title": "Dhimmel/Gwas-Catalog V1.0: Extracting Gene–Disease Associations From The Gwas Catalog", - "URL": "https://doi.org/10.5281/zenodo.48428", - "short_url": "https://doi.org/f3mqws" + "URL": "https://doi.org/f3mqws" }, { "type": "article", "id": "rTcQZ8h2", + "categories": [ + "PPI", + "proteins", + "interactions", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26052,12 +10136,17 @@ "DOI": "10.5281/zenodo.48443", "publisher": "Zenodo", "title": "Dhimmel/Ppi V1.0: Compiling A Human Protein Interaction Catalog", - "URL": "https://doi.org/10.5281/zenodo.48443", - "short_url": "https://doi.org/f3mqtw" + "URL": "https://doi.org/f3mqtw" }, { "type": "article", "id": "IZHE5ysl", + "categories": [ + "evolution", + "ERC", + "evolutionary rate covariation", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26077,12 +10166,14 @@ "DOI": "10.5281/zenodo.48444", "publisher": "Zenodo", "title": "Dhimmel/Erc V1.0: Processing Human Evolutionary Rate Covaration Data", - "URL": "https://doi.org/10.5281/zenodo.48444", - "short_url": "https://doi.org/f3mqwm" + "URL": "https://doi.org/f3mqwm" }, { "type": "article", "id": "ZXg5KPir", + "categories": [ + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26102,12 +10193,19 @@ "DOI": "10.5281/zenodo.48445", "publisher": "Zenodo", "title": "Dhimmel/Medline V1.0: Disease, Symptom, And Anatomy Cooccurence In Medline", - "URL": "https://doi.org/10.5281/zenodo.48445", - "short_url": "https://doi.org/f3mqts" + "URL": "https://doi.org/f3mqts" }, { "type": "article", "id": "HDkm1eac", + "categories": [ + "pathways", + "gene set", + "WikiPathways", + "Reactome", + "PID", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26131,12 +10229,18 @@ "DOI": "10.5281/zenodo.48810", "publisher": "Zenodo", "title": "Dhimmel/Pathways V2.0: Compiling Human Pathway Gene Sets", - "URL": "https://doi.org/10.5281/zenodo.48810", - "short_url": "https://doi.org/bfq3" + "URL": "https://doi.org/bfq3" }, { "type": "article", "id": "1GJM7558r", + "categories": [ + "hetnet", + "network", + "Neo4j", + "heterogeneous network", + "Rephetio" + ], "author": [ { "family": "Himmelstein", @@ -26155,85 +10259,29 @@ "abstract": "This release provides some bugfixes as well as many new features. Development for this release occurred during work on Project Rephetio. This is the first release that has been uploaded to PyPI.\n\nNew modules\n\n\n\t\n\tThe new neo4j module provides support for exporting a hetio.graph into Neo4j. The module also supports creating DWPC Cypher queries from a metagraph. See https://doi.org/10.15363/thinklab.d112 for more information on our adoption of Neo4j.\n\t\n\t\n\tThe new permute module provides hetnet permutation functionality. For an example use, see https://doi.org/10.15363/thinklab.d178.\n\t\n\t\n\tThe new stats module produces user-friendly TSVs and visualizations with hetnet summary stats.\n\t\n\n\nEnhancements\n\n\n\t\n\tMetaGraphs now have a metapath_from_abbrev method which takes a metapath abbreviation str and returns the corresponding metapath object.\n\t\n\t\n\tThe hetio.readwrite module now supports reading metagraphs and graphs from a (compressed) URL.\n\t\n\t\n\tPretty formatting of (meta)edges and (meta)paths using the get_unicode_str method.\n\t\n\t\n\tSupport for Graph Modeling Language (GML) was removed to do the poor documentation and esoterism of this format.\n\t\n\t\n\tImproved abbreviation module.", "DOI": "10.5281/zenodo.61571", "publisher": "Zenodo", - "title": "dhimmel/hetio v0.2.0: Neo4j export, Cypher query creation, hetnet stats, and other enhancements", - "URL": "https://doi.org/10.5281/zenodo.61571", - "short_url": "https://doi.org/gbr42q" + "title": "Dhimmel/Hetio V0.2.0: Neo4J Export, Cypher Query Creation, Hetnet Stats, And Other Enhancements", + "URL": "https://doi.org/gbr42q" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 11 - ] - ], - "date-time": "2017-08-11T06:50:35Z", - "timestamp": 1502434235829 - }, - "reference-count": 31, "publisher": "Ubiquity Press, Ltd.", - "issue": "1", - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, - "published-print": { - "date-parts": [ - [ - 2014, - 7, - 9 - ] - ] - }, - "DOI": "10.5334/jors.ay", - "type": "article-journal", - "created": { - "date-parts": [ - [ - 2014, - 7, - 9 - ] - ], - "date-time": "2014-07-09T12:00:22Z", - "timestamp": 1404907222000 - }, + "issue": "1", + "DOI": "10.5334/jors.ay", + "type": "article-journal", "source": "Crossref", - "is-referenced-by-count": 18, "title": "Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research", - "prefix": "10.5334", "volume": "2", "author": [ { "given": "Victoria", - "family": "Stodden", - "affiliation": [] + "family": "Stodden" }, { "given": "Sheila", - "family": "Miguez", - "affiliation": [] + "family": "Miguez" } ], - "member": "3285", "container-title": "Journal of Open Research Software", - "original-title": [], - "deposited": { - "date-parts": [ - [ - 2017, - 6, - 22 - ] - ], - "date-time": "2017-06-22T16:26:04Z", - "timestamp": 1498148764000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -26243,152 +10291,34 @@ ] ] }, - "references-count": 31, - "alternative-id": [ - "10.5334/jors.ay" - ], - "URL": "https://doi.org/10.5334/jors.ay", - "relation": { - "cites": [] - }, - "short_url": "https://doi.org/f3mn69", + "URL": "https://doi.org/f3mn69", "id": "fL5DrqXg" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 11 - ] - ], - "date-time": "2017-08-11T03:53:57Z", - "timestamp": 1502423637066 - }, - "reference-count": 38, - "publisher": "Korea Genome Organization (KAMJE)", + "publisher": "Korea Genome Organization", "issue": "1", - "license": [ - { - "URL": "http://creativecommons.org/licenses/by-nc/4.0/", - "start": { - "date-parts": [ - [ - 2017, - 1, - 1 - ] - ], - "date-time": "2017-01-01T00:00:00Z", - "timestamp": 1483228800000 - }, - "delay-in-days": 0, - "content-version": "tdm" - } - ], - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, - "published-print": { - "date-parts": [ - [ - 2017 - ] - ] - }, "DOI": "10.5808/gi.2017.15.1.19", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2017, - 4, - 10 - ] - ], - "date-time": "2017-04-10T05:20:40Z", - "timestamp": 1491801640000 - }, "page": "19", "source": "Crossref", - "is-referenced-by-count": 0, "title": "Use of Graph Database for the Integration of Heterogeneous Biological Data", - "prefix": "10.5808", "volume": "15", "author": [ { "given": "Byoung-Ha", - "family": "Yoon", - "affiliation": [ - { - "name": "Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea." - }, - { - "name": "Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea." - } - ] + "family": "Yoon" }, { "given": "Seon-Kyu", - "family": "Kim", - "affiliation": [ - { - "name": "Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea." - } - ] + "family": "Kim" }, { "given": "Seon-Young", - "family": "Kim", - "affiliation": [ - { - "name": "Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea." - }, - { - "name": "Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea." - } - ] + "family": "Kim" } ], - "member": "3815", "container-title": "Genomics & Informatics", - "original-title": [], - "link": [ - { - "URL": "https://synapse.koreamed.org/pdf/10.5808/GI.2017.15.1.19", - "content-type": "application/pdf", - "content-version": "vor", - "intended-application": "text-mining" - }, - { - "URL": "https://synapse.koreamed.org/DOIx.php?id=10.5808/GI.2017.15.1.19", - "content-type": "unspecified", - "content-version": "vor", - "intended-application": "text-mining" - }, - { - "URL": "https://synapse.koreamed.org/DOIx.php?id=10.5808/GI.2017.15.1.19", - "content-type": "unspecified", - "content-version": "vor", - "intended-application": "similarity-checking" - } - ], - "deposited": { - "date-parts": [ - [ - 2017, - 6, - 25 - ] - ], - "date-time": "2017-06-25T14:31:57Z", - "timestamp": 1498401117000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -26396,11 +10326,10 @@ ] ] }, - "references-count": 38, - "URL": "https://doi.org/10.5808/gi.2017.15.1.19", - "relation": {}, + "URL": "https://doi.org/f93xct", "container-title-short": "Genomics Inform", - "short_url": "https://doi.org/f93xct", + "PMCID": "PMC5389944", + "PMID": "28416946", "id": "LkO4BC2X" }, { @@ -26430,8 +10359,7 @@ "DOI": "10.6084/m9.figshare.1418386", "publisher": "Figshare", "title": "Pairwise molecular similarities between DrugBank compounds", - "URL": "https://doi.org/10.6084/m9.figshare.1418386", - "short_url": "https://doi.org/f3mqt2" + "URL": "https://doi.org/f3mqt2" }, { "type": "article", @@ -26460,8 +10388,7 @@ "DOI": "10.6084/m9.figshare.3085426.v1", "publisher": "Figshare", "title": "Consensus signatures for LINCS L1000 perturbations", - "URL": "https://doi.org/10.6084/m9.figshare.3085426.v1", - "short_url": "https://doi.org/f3mqvs" + "URL": "https://doi.org/f3mqvs" }, { "type": "dataset", @@ -26490,8 +10417,7 @@ "DOI": "10.6084/m9.figshare.3085837.v1", "publisher": "Figshare", "title": "l1000.db: SQLite database of LINCS L1000 metadata", - "URL": "https://doi.org/10.6084/m9.figshare.3085837.v1", - "short_url": "https://doi.org/f3mqtq" + "URL": "https://doi.org/f3mqtq" }, { "type": "article", @@ -26527,12 +10453,17 @@ "DOI": "10.6084/m9.figshare.3103054", "publisher": "Figshare", "title": "PharmacotherapyDB 1.0: the open catalog of drug therapies for disease", - "URL": "https://doi.org/10.6084/m9.figshare.3103054", - "short_url": "https://doi.org/f3mqvq" + "URL": "https://doi.org/f3mqvq" }, { "type": "article", "id": "JTRRKBmL", + "categories": [ + "Bioinformatics", + "Pharmacology", + "Diseases", + "Pathogenesis" + ], "author": [ { "family": "Himmelstein", @@ -26558,231 +10489,80 @@ "DOI": "10.6084/m9.figshare.c.2861359.v1", "publisher": "Figshare", "title": "Figshare depositions from Project Rephetio", - "URL": "https://doi.org/10.6084/m9.figshare.c.2861359.v1", - "short_url": "https://doi.org/ccq3" + "URL": "https://doi.org/ccq3" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 31 - ] - ], - "date-time": "2017-08-31T13:22:15Z", - "timestamp": 1504185735661 - }, - "reference-count": 157, - "publisher": "eLife Sciences Organisation, Ltd.", - "license": [ - { - "URL": "http://creativecommons.org/licenses/by/4.0/", - "start": { - "date-parts": [ - [ - 2016, - 7, - 7 - ] - ], - "date-time": "2016-07-07T00:00:00Z", - "timestamp": 1467849600000 - }, - "delay-in-days": 0, - "content-version": "vor" - }, - { - "URL": "http://creativecommons.org/licenses/by/4.0/", - "start": { - "date-parts": [ - [ - 2016, - 7, - 7 - ] - ], - "date-time": "2016-07-07T00:00:00Z", - "timestamp": 1467849600000 - }, - "delay-in-days": 0, - "content-version": "am" - }, - { - "URL": "http://creativecommons.org/licenses/by/4.0/", - "start": { - "date-parts": [ - [ - 2016, - 7, - 7 - ] - ], - "date-time": "2016-07-07T00:00:00Z", - "timestamp": 1467849600000 - }, - "delay-in-days": 0, - "content-version": "tdm" - } - ], - "funder": [ - { - "DOI": "10.13039/100000049", - "name": "National Institute on Aging", - "doi-asserted-by": "publisher", - "award": [ - "R24AG048124" - ] - }, - { - "DOI": "10.13039/100009827", - "name": "Laura and John Arnold Foundation", - "doi-asserted-by": "crossref", - "award": [] - }, - { - "DOI": "10.13039/100000925", - "name": "John Templeton Foundation", - "doi-asserted-by": "publisher", - "award": [ - "46545" - ] - } - ], - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, + "publisher": "eLife Sciences Publications, Ltd", "abstract": "Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.", "DOI": "10.7554/elife.16800", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2016, - 7, - 7 - ] - ], - "date-time": "2016-07-07T17:46:41Z", - "timestamp": 1467913601000 - }, "source": "Crossref", - "is-referenced-by-count": 21, "title": "How open science helps researchers succeed", - "prefix": "10.7554", "volume": "5", "author": [ { - "ORCID": "http://orcid.org/0000-0002-9430-5221", - "authenticated-orcid": true, "given": "Erin C", - "family": "McKiernan", - "affiliation": [] + "family": "McKiernan" }, { - "ORCID": "http://orcid.org/0000-0002-7618-7292", - "authenticated-orcid": true, "given": "Philip E", - "family": "Bourne", - "affiliation": [] + "family": "Bourne" }, { "given": "C Titus", - "family": "Brown", - "affiliation": [] + "family": "Brown" }, { "given": "Stuart", - "family": "Buck", - "affiliation": [] + "family": "Buck" }, { - "ORCID": "http://orcid.org/0000-0002-3030-8001", - "authenticated-orcid": true, "given": "Amye", - "family": "Kenall", - "affiliation": [] + "family": "Kenall" }, { "given": "Jennifer", - "family": "Lin", - "affiliation": [] + "family": "Lin" }, { "given": "Damon", - "family": "McDougall", - "affiliation": [] + "family": "McDougall" }, { "given": "Brian A", - "family": "Nosek", - "affiliation": [] + "family": "Nosek" }, { "given": "Karthik", - "family": "Ram", - "affiliation": [] + "family": "Ram" }, { "given": "Courtney K", - "family": "Soderberg", - "affiliation": [] + "family": "Soderberg" }, { "given": "Jeffrey R", - "family": "Spies", - "affiliation": [] + "family": "Spies" }, { "given": "Kaitlin", - "family": "Thaney", - "affiliation": [] + "family": "Thaney" }, { "given": "Andrew", - "family": "Updegrove", - "affiliation": [] + "family": "Updegrove" }, { - "ORCID": "http://orcid.org/0000-0002-5125-4188", - "authenticated-orcid": true, "given": "Kara H", - "family": "Woo", - "affiliation": [] + "family": "Woo" }, { "given": "Tal", - "family": "Yarkoni", - "affiliation": [] + "family": "Yarkoni" } ], - "member": "4374", - "published-online": { - "date-parts": [ - [ - 2016, - 7, - 7 - ] - ] - }, "container-title": "eLife", - "original-title": [], - "deposited": { - "date-parts": [ - [ - 2016, - 7, - 29 - ] - ], - "date-time": "2016-07-29T12:59:29Z", - "timestamp": 1469797169000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -26792,166 +10572,36 @@ ] ] }, - "references-count": 157, - "alternative-id": [ - "10.7554/eLife.16800" - ], - "URL": "https://doi.org/10.7554/elife.16800", - "relation": { - "cites": [] - }, - "subject": [ - "General Biochemistry, Genetics and Molecular Biology", - "General Immunology and Microbiology", - "General Neuroscience", - "General Medicine" - ], - "short_url": "https://doi.org/gbqsng", + "URL": "https://doi.org/gbqsng", + "PMCID": "PMC4973366", + "PMID": "27387362", "id": "HQfvK1OF" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 9 - ] - ], - "date-time": "2017-08-09T02:48:33Z", - "timestamp": 1502246913635 - }, - "reference-count": 92, "publisher": "PeerJ", - "license": [ - { - "URL": "http://creativecommons.org/licenses/by/4.0/", - "start": { - "date-parts": [ - [ - 2016, - 2, - 24 - ] - ], - "date-time": "2016-02-24T00:00:00Z", - "timestamp": 1456272000000 - }, - "delay-in-days": 0, - "content-version": "vor" - } - ], - "funder": [ - { - "name": "Thomson Reuters Global Resources", - "award": [] - } - ], - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, "abstract": "Drug repositioning methods attempt to identify novel therapeutic indications for marketed drugs. Strategies include the use of side-effects to assign new disease indications, based on the premise that both therapeutic effects and side-effects are measurable physiological changes resulting from drug intervention. Drugs with similar side-effects might share a common mechanism of action linking side-effects with disease treatment, or may serve as a treatment by “rescuing” a disease phenotype on the basis of their side-effects; therefore it may be possible to infer new indications based on the similarity of side-effect profiles. While existing methods leverage side-effect data from clinical studies and drug labels, evidence suggests this information is often incomplete due to under-reporting. Here, we describe a novel computational method that uses side-effect data mined from social media to generate a sparse undirected graphical model using inverse covariance estimation with ℓ1-norm regularization. Results show that known indications are well recovered while current trial indications can also be identified, suggesting that sparse graphical models generated using side-effect data mined from social media may be useful for computational drug repositioning.", "DOI": "10.7717/peerj-cs.46", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2016, - 2, - 24 - ] - ], - "date-time": "2016-02-24T08:49:20Z", - "timestamp": 1456303760000 - }, "page": "e46", "source": "Crossref", - "is-referenced-by-count": 4, "title": "Computational drug repositioning based on side-effects mined from social media", - "prefix": "10.7717", "volume": "2", "author": [ { "given": "Timothy", - "family": "Nugent", - "affiliation": [ - { - "name": "Thomson Reuters, Corporate Research and Development, London, United Kingdom" - } - ] + "family": "Nugent" }, { "given": "Vassilis", - "family": "Plachouras", - "affiliation": [ - { - "name": "Thomson Reuters, Corporate Research and Development, London, United Kingdom" - } - ] + "family": "Plachouras" }, { "given": "Jochen L.", - "family": "Leidner", - "affiliation": [ - { - "name": "Thomson Reuters, Corporate Research and Development, London, United Kingdom" - } - ] + "family": "Leidner" } ], - "member": "4443", - "published-online": { - "date-parts": [ - [ - 2016, - 2, - 24 - ] - ] - }, "container-title": "PeerJ Computer Science", - "original-title": [], - "link": [ - { - "URL": "https://peerj.com/articles/cs-46.pdf", - "content-type": "application/pdf", - "content-version": "vor", - "intended-application": "text-mining" - }, - { - "URL": "https://peerj.com/articles/cs-46.xml", - "content-type": "application/xml", - "content-version": "vor", - "intended-application": "text-mining" - }, - { - "URL": "https://peerj.com/articles/cs-46.html", - "content-type": "text/html", - "content-version": "vor", - "intended-application": "text-mining" - }, - { - "URL": "https://peerj.com/articles/cs-46.pdf", - "content-type": "unspecified", - "content-version": "vor", - "intended-application": "similarity-checking" - } - ], - "deposited": { - "date-parts": [ - [ - 2017, - 6, - 24 - ] - ], - "date-time": "2017-06-24T06:40:45Z", - "timestamp": 1498286445000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -26961,148 +10611,45 @@ ] ] }, - "references-count": 92, - "alternative-id": [ - "10.7717/peerj-cs.46" - ], - "URL": "https://doi.org/10.7717/peerj-cs.46", - "relation": { - "cites": [] - }, - "article-number": "e46", - "short_url": "https://doi.org/f3mn54", + "URL": "https://doi.org/f3mn54", "id": "1GXwDMvjL" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 10 - ] - ], - "date-time": "2017-08-10T15:46:23Z", - "timestamp": 1502379983468 - }, - "reference-count": 62, "publisher": "PeerJ", - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, "DOI": "10.7717/peerj.1054", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2015, - 6, - 30 - ] - ], - "date-time": "2015-06-30T08:23:30Z", - "timestamp": 1435652610000 - }, "page": "e1054", "source": "Crossref", - "is-referenced-by-count": 16, "title": "Comprehensive comparison of large-scale tissue expression datasets", - "prefix": "10.7717", "volume": "3", "author": [ { "given": "Alberto", - "family": "Santos", - "affiliation": [ - { - "name": "Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark" - } - ] + "family": "Santos" }, { "given": "Kalliopi", - "family": "Tsafou", - "affiliation": [ - { - "name": "Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark" - } - ] + "family": "Tsafou" }, { "given": "Christian", - "family": "Stolte", - "affiliation": [ - { - "name": "Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia" - } - ] + "family": "Stolte" }, { "given": "Sune", - "family": "Pletscher-Frankild", - "affiliation": [ - { - "name": "Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark" - } - ] + "family": "Pletscher-Frankild" }, { "given": "Seán I.", - "family": "O’Donoghue", - "affiliation": [ - { - "name": "Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia" - }, - { - "name": "Garvan Institute of Medical Research, Sydney, Australia" - } - ] + "family": "O’Donoghue" }, { "given": "Lars Juhl", - "family": "Jensen", - "affiliation": [ - { - "name": "Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark" - } - ] + "family": "Jensen" } ], - "member": "4443", - "published-online": { - "date-parts": [ - [ - 2015, - 6, - 30 - ] - ] - }, "container-title": "PeerJ", - "original-title": [], - "link": [ - { - "URL": "https://peerj.com/articles/1054.pdf", - "content-type": "unspecified", - "content-version": "vor", - "intended-application": "similarity-checking" - } - ], - "deposited": { - "date-parts": [ - [ - 2016, - 12, - 19 - ] - ], - "date-time": "2016-12-19T17:14:52Z", - "timestamp": 1482167692000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -27112,130 +10659,31 @@ ] ] }, - "references-count": 62, - "alternative-id": [ - "10.7717/peerj.1054" - ], - "URL": "https://doi.org/10.7717/peerj.1054", - "relation": { - "has-preprint": [ - { - "id-type": "doi", - "id": "10.1101/010975", - "asserted-by": "object" - } - ], - "cites": [] - }, - "subject": [ - "General Biochemistry, Genetics and Molecular Biology", - "General Neuroscience", - "General Agricultural and Biological Sciences", - "General Medicine" - ], - "short_url": "https://doi.org/f3mn6p", + "URL": "https://doi.org/f3mn6p", + "PMCID": "PMC4493645", + "PMID": "26157623", "id": "6QECA6Hm" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 31 - ] - ], - "date-time": "2017-08-31T13:22:15Z", - "timestamp": 1504185735665 - }, - "reference-count": 35, "publisher": "PeerJ", - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, "DOI": "10.7717/peerj.175", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2013, - 10, - 1 - ] - ], - "date-time": "2013-10-01T09:06:40Z", - "timestamp": 1380618400000 - }, "page": "e175", "source": "Crossref", - "is-referenced-by-count": 63, "title": "Data reuse and the open data citation advantage", - "prefix": "10.7717", "volume": "1", "author": [ { "given": "Heather A.", - "family": "Piwowar", - "affiliation": [ - { - "name": "National Evolutionary Synthesis Center, Durham, NC, USA" - }, - { - "name": "Department of Biology, Duke University, Durham, NC, USA" - } - ] + "family": "Piwowar" }, { "given": "Todd J.", - "family": "Vision", - "affiliation": [ - { - "name": "National Evolutionary Synthesis Center, Durham, NC, USA" - }, - { - "name": "Department of Biology, Duke University, Durham, NC, USA" - }, - { - "name": "Department of Biology, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA" - } - ] + "family": "Vision" } ], - "member": "4443", - "published-online": { - "date-parts": [ - [ - 2013, - 10, - 1 - ] - ] - }, "container-title": "PeerJ", - "original-title": [], - "link": [ - { - "URL": "https://peerj.com/articles/175.pdf", - "content-type": "unspecified", - "content-version": "vor", - "intended-application": "similarity-checking" - } - ], - "deposited": { - "date-parts": [ - [ - 2017, - 6, - 21 - ] - ], - "date-time": "2017-06-21T22:53:18Z", - "timestamp": 1498085598000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], + "language": "en", "issued": { "date-parts": [ [ @@ -27245,93 +10693,27 @@ ] ] }, - "references-count": 35, - "alternative-id": [ - "10.7717/peerj.175" - ], - "URL": "https://doi.org/10.7717/peerj.175", - "relation": { - "cites": [] - }, - "subject": [ - "General Biochemistry, Genetics and Molecular Biology", - "General Neuroscience", - "General Agricultural and Biological Sciences", - "General Medicine" - ], - "short_url": "https://doi.org/f3mn68", + "URL": "https://doi.org/f3mn68", + "PMCID": "PMC3792178", + "PMID": "24109559", "id": "g8J7Zkoz" }, { - "indexed": { - "date-parts": [ - [ - 2017, - 8, - 10 - ] - ], - "date-time": "2017-08-10T00:18:40Z", - "timestamp": 1502324320636 - }, - "reference-count": 0, "publisher": "IOSR Journals", "issue": "08", - "content-domain": { - "domain": [], - "crossmark-restriction": false - }, - "published-print": { - "date-parts": [ - [ - 2013, - 8 - ] - ] - }, "DOI": "10.9790/3021-03822527", "type": "article-journal", - "created": { - "date-parts": [ - [ - 2013, - 9, - 16 - ] - ], - "date-time": "2013-09-16T11:40:28Z", - "timestamp": 1379331628000 - }, "page": "25-27", "source": "Crossref", - "is-referenced-by-count": 0, "title": "Comparative analysis of Relational and Graph databases", - "prefix": "10.9790", "volume": "03", "author": [ { "given": "Garima", - "family": "Jaiswal", - "affiliation": [] + "family": "Jaiswal" } ], - "member": "4786", "container-title": "IOSR Journal of Engineering", - "original-title": [], - "deposited": { - "date-parts": [ - [ - 2013, - 9, - 16 - ] - ], - "date-time": "2013-09-16T11:40:33Z", - "timestamp": 1379331633000 - }, - "score": 1.0, - "subtitle": [], - "short-title": [], "issued": { "date-parts": [ [ @@ -27340,11 +10722,8 @@ ] ] }, - "references-count": 0, - "URL": "https://doi.org/10.9790/3021-03822527", - "relation": {}, + "URL": "https://doi.org/gbr42z", "container-title-short": "IOSRJEN", - "short_url": "https://doi.org/gbr42z", "id": "4NONxFr3" } ] diff --git a/variables.json b/variables.json index 58ce139..cd89a2f 100644 --- a/variables.json +++ b/variables.json @@ -1,5 +1,5 @@ { - "date": "October 3, 2017", + "date": "March 14, 2019", "authors": [ { "github": "dhimmel", @@ -7,7 +7,14 @@ "orcid": "0000-0002-3012-7446", "twitter": "dhimmel", "email": "daniel.himmelstein@gmail.com", - "affiliations": "Program\u00a0in\u00a0Biological\u00a0&\u00a0Medical\u00a0Informatics,\u00a0University\u00a0of\u00a0California,\u00a0San\u00a0Francisco; Department\u00a0of\u00a0Systems\u00a0Pharmacology\u00a0&\u00a0Translational\u00a0Therapeutics,\u00a0University\u00a0of\u00a0Pennsylvania" + "affiliations": [ + "Program\u00a0in\u00a0Biological\u00a0&\u00a0Medical\u00a0Informatics,\u00a0University\u00a0of\u00a0California,\u00a0San\u00a0Francisco", + "Department\u00a0of\u00a0Systems\u00a0Pharmacology\u00a0&\u00a0Translational\u00a0Therapeutics,\u00a0University\u00a0of\u00a0Pennsylvania" + ], + "affiliation_numbers": [ + 1, + 2 + ] }, { "github": "antoine-lizee", @@ -15,59 +22,143 @@ "orcid": "0000-0002-1073-3190", "twitter": "A_Lizee", "email": "antoine.lizee@gmail.com", - 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