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.">

BioM2: Biologically Explainable Machine Learning Framework (original) (raw)

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: 1.1.2
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: 2025-05-14
DOI: 10.32614/CRAN.package.BioM2
Author: Shunjie Zhang [aut, cre], Junfang Chen [aut]
Maintainer: Shunjie Zhang <zhang.shunjie at qq.com>
License: MIT + file
NeedsCompilation: no
Materials: README
CRAN checks: BioM2 results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=BioM2to link to this page.