## BioMM BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data ## Features of BioMM in a nutshell 1. Applicability for various omics data modalities (e.g. methylome, transcriptomics, genomics). 2. Various biological stratification strategies. 3. Prioritizing outcome-associated functional patterns. 4. End-to-end prediction at the individual level based on biological stratified patterns. 4. Possibility for an extension to machine learning models of interest. 6. Parallel computing. ## Installation BioMM installation from Github ```{r eval=FALSE} install.packages("devtools") library("devtools") install_github("transbioZI/BioMM", build_vignettes=TRUE) ``` BioMM has been incorporated into the [Bioconductor](http://www.bioconductor.org/packages/devel/bioc//html/BioMM.html). To install this package from BioConductor, start R (version "4.0") and enter: ```{r eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BioMM", version = "4.0") ``` ## Tutorial The detailed instructions on how to use this package are explained in the most updated [vignette](https://bioconductor.org/packages/devel/bioc/vignettes/BioMM/inst/doc/BioMMtutorial.html). ## Citation NIPS ML4H submission: Chen, J. and Schwarz, E., 2017. BioMM: Biologically-informed Multi-stage Machine learning for identification of epigenetic fingerprints. arXiv preprint arXiv:1712.00336. Chen, Junfang, et al. "Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function." JAMA psychiatry (2020).