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 |
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