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README.Rmd
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---
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)
```
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### fuseMLR
Cesaire J. K. Fouodo
### Introduction
Recent technological advances have enabled the simultaneous collection of multi-omics data, i.e., different types or modalities of molecular data across various organ tissues of patients. For integrative predictive modeling, the analysis of such data is particularly challenging. Ideally, data from the different modalities are measured in the same individuals, allowing for early or intermediate integrative techniques. However, they are often not applicable when patient data only partially overlap, which requires either reducing the datasets or imputing missing values. Additionally, the characteristics of each data modality may necessitate specific statistical methods rather than applying the same method across all modalities. Late integration modeling approaches analyze each data modality separately to obtain modality-specific predictions. These predictions are then integrated to train aggregative models like Lasso, random forests, or compute the weighted mean of modality-specific predictions.
We introduce the R package fuseMLR for late integration prediction modeling. This comprehensive package enables users to define a training process with multiple data modalities and modality-specific machine learning methods. The package is user-friendly, facilitates variable selection and training of different models across modalities, and automatically performs aggregation once modality-specific training is completed. We simulated multi-omics data to illustrate the usage of our new package for conducting late-stage multi-omics integrative modeling.
`fuseMLR` is an object-oriented package based on `R6` version 2.5.1. Refer to the vignette (section *Usage* below) for a quick overview of how to use the package.
The following figure illustrates the general architecture of `fuseMLR`:
```{r, echo=FALSE, out.width="70%", out.height="100%"}
knitr::include_graphics("README_files/figure-gfm/structure.png")
```
The general architecture of `fuseMLR` includes the collection classes `Training`, `TrainLayer`, and `TrainMetaLayer`. `TrainLayer` and `TrainMetaLayer` are stored within a `Training` instance, while `TrainData`, `Lrner`, and `VarSel` (for variable selection) are stored within a `TrainLayer` or `MetaTrainLayer` instance. An `Training` object can be used to automatically conduct layer-specific variable selection and train layer-specfic learner and the meta-learner. Analogously, a `Testing` can be set up and predicted after the training.
### Installation
Install the release version (v0.0.1) from CRAN with
```R
install.packages("fuseMLR")
```
Install the development version from GitHub with
```R
devtools::install_github("imbs-hl/fuseMLR")
```
### Usage
Refer to our vignette to understand [how fuseMLR works](https://cran.r-project.org/web/packages/fuseMLR/vignettes/fuseMLR.html).
© 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved.