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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 |
| Reference manual: | BioM2.pdf |
| Package source: | BioM2_1.0.6.tar.gz |
| Windows binaries: | r-devel: BioM2_1.0.6.zip, r-release: BioM2_1.0.6.zip, 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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