From fc35ac09191c6e72b21f0c2d02b66dbe187030ac Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Wed, 17 Jul 2024 14:56:17 +0200 Subject: [PATCH] Add README.md --- README.Rmd | 9 ++ README.html | 431 ---------------------------------------------------- README.md | 57 ++++--- 3 files changed, 36 insertions(+), 461 deletions(-) delete mode 100644 README.html diff --git a/README.Rmd b/README.Rmd index d796331..02ef439 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,3 +1,12 @@ +--- +title: "fuseMLR" +author: Cesaire J. K. Fouodo +output: + md_document: + variant: gfm + preserve_yaml: true +--- + ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` diff --git a/README.html b/README.html deleted file mode 100644 index 37bef4d..0000000 --- a/README.html +++ /dev/null @@ -1,431 +0,0 @@ - - - - - - - - - - - - - -README.knit - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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R-CMD-check Lifecycle: experimental CRAN downloads Stack Overflow

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fuseMLR

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Cesaire J. K. Fouodo

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Introduction

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Recent technological advances have enabled the simultaneous targeting -of multiple pathways to enhance therapies for complex diseases. This -often results in the collection of numerous data entities across various -layers of patient groups, posing a challenge for integrating all data -into a single analysis. Ideally, patient data will overlap across -layers, allowing for early or intermediate integrative techniques. -However, these techniques are challenging when patient data does not -overlap well. Additionally, the internal structure of each data entity -may necessitate specific statistical methods rather than applying the -same method across all layers. Late integration modeling addresses this -by analyzing each data entity separately to obtain layer-specific -results, which are then integrated using meta-analysis. Currently, no R -package offers this flexibility.

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We introduce the fuseMLR package for late integration modeling in R. -This package allows users to define studies with multiple layers, data -entities, and layer-specific machine learning methods. FuseMLR is -user-friendly, enabling the training of different models across layers -and automatically conducting meta-analysis once layer-specific training -is completed. Additionally, fuseMLR allows for variable selection at the -layer level and makes predictions for new data entities.

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- - - - - - - - - - - - - - - diff --git a/README.md b/README.md index ffa54e4..a713af8 100644 --- a/README.md +++ b/README.md @@ -18,33 +18,30 @@ downloads](http://www.r-pkg.org/badges/version/fuseMLR)](http://cranlogs.r-pkg.o Overflow](https://img.shields.io/badge/stackoverflow-questions-orange.svg)](https://stackoverflow.com/questions/tagged/fuseMLR) -## R Markdown - -This is an R Markdown document. Markdown is a simple formatting syntax -for authoring HTML, PDF, and MS Word documents. For more details on -using R Markdown see . - -When you click the **Knit** button a document will be generated that -includes both content as well as the output of any embedded R code -chunks within the document. You can embed an R code chunk like this: - -``` r -summary(cars) -``` - - ## speed dist - ## Min. : 4.0 Min. : 2.00 - ## 1st Qu.:12.0 1st Qu.: 26.00 - ## Median :15.0 Median : 36.00 - ## Mean :15.4 Mean : 42.98 - ## 3rd Qu.:19.0 3rd Qu.: 56.00 - ## Max. :25.0 Max. :120.00 - -## Including Plots - -You can also embed plots, for example: - -![](README_files/figure-gfm/pressure-1.png) - -Note that the `echo = FALSE` parameter was added to the code chunk to -prevent printing of the R code that generated the plot. +### fuseMLR + +Cesaire J. K. Fouodo + +### Introduction + +Recent technological advances have enabled the simultaneous targeting of +multiple pathways to enhance therapies for complex diseases. This often +results in the collection of numerous data entities across various +layers of patient groups, posing a challenge for integrating all data +into a single analysis. Ideally, patient data will overlap across +layers, allowing for early or intermediate integrative techniques. +However, these techniques are challenging when patient data does not +overlap well. Additionally, the internal structure of each data entity +may necessitate specific statistical methods rather than applying the +same method across all layers. Late integration modeling addresses this +by analyzing each data entity separately to obtain layer-specific +results, which are then integrated using meta-analysis. Currently, no R +package offers this flexibility. + +We introduce the fuseMLR package for late integration modeling in R. +This package allows users to define studies with multiple layers, data +entities, and layer-specific machine learning methods. FuseMLR is +user-friendly, enabling the training of different models across layers +and automatically conducting meta-analysis once layer-specific training +is completed. Additionally, fuseMLR allows for variable selection at the +layer level and makes predictions for new data entities.