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## Source
The manuscripts in this directory were built from
-[`5358af54ea83dcabb3eed3805ba87455b00f5bf8`](https://github.com/dhimmel/rephetio-manuscript/commit/5358af54ea83dcabb3eed3805ba87455b00f5bf8).
+[`7b5b0e67fb9ab159c84c230dd5c07001941d82af`](https://github.com/dhimmel/rephetio-manuscript/commit/7b5b0e67fb9ab159c84c230dd5c07001941d82af).
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
- This manuscript was automatically generated from dhimmel/rephetio-manuscript@5358af5 on September 22, 2017.
+ This manuscript was automatically generated from dhimmel/rephetio-manuscript@7b5b0e6 on October 3, 2017.
Authors
Daniel S. Himmelstein
0000-0002-3012-7446 · 
dhimmel · 
dhimmel
Program in Biological & Medical Informatics, University of California, San Francisco; Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania
Systematic mechanisms of 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].](https://github.com/dhimmel/learn/raw/ef5f7a6b76b6a01499d65b95e3d7ca93ac5aba57/prediction/figure/features.png)
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.
+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 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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