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: 1.0.6
Depends: R (≥ 4.1.0)
Imports: WGCNA, mlr3, CMplot, ggsci, ROCR, caret, ggplot2, ggpubr, viridis, ggthemes, ggstatsplot, htmlwidgets, jiebaR, mlr3verse, parallel, uwot, webshot, wordcloud2, ggforce, igraph, ggnetwork
Published: 2024-05-16
Author: Shunjie Zhang and Junfang Chen
Maintainer: Shunjie Zhang <zhang.shunjie at qq.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: BioM2 results

Documentation:

Reference manual: BioM2.pdf

Downloads:

Package source: BioM2_1.0.6.tar.gz
Windows binaries: r-devel: BioM2_1.0.6.zip, r-release: not available, r-oldrel: BioM2_1.0.6.zip
macOS binaries: r-release (arm64): BioM2_1.0.6.tgz, r-oldrel (arm64): BioM2_1.0.6.tgz, r-release (x86_64): BioM2_1.0.6.tgz, r-oldrel (x86_64): BioM2_1.0.6.tgz
Old sources: BioM2 archive

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