Biologically Explainable Machine Learning Framework R package BioM2 version 1.1.3 Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods. Software Depends: R (>= 4.1.0) Imports: WGCNA, mlr3, CMplot, ggsci, ROCR, caret, ggplot2, ggpubr, viridis, ggthemes, ggstatsplot, htmlwidgets, mlr3verse, parallel, uwot, webshot, wordcloud2,ggforce, igraph, ggnetwork Shunjie Zhang <zhang.shunjie@qq.com> Comprehensive R Archive Network (CRAN) Shunjie Zhang [aut, cre], Junfang Chen [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=BioM2/LICENSE) 2025-07-17 application/tgz https://CRAN.R-project.org/package=BioM2 doi:10.32614/CRAN.package.BioM2