BioM2: Biologically Explainable Machine Learning Framework
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.
| Version: | 1.1.3 |
| 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 |
| Published: | 2025-07-17 |
| DOI: | 10.32614/CRAN.package.BioM2 |
| Author: | Shunjie Zhang [aut, cre], Junfang Chen [aut] |
| Maintainer: | Shunjie Zhang <zhang.shunjie at qq.com> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | BioM2 results |
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