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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.3 |
rolling source/ R- | BioM2_1.1.3.tar.gz |
3.2 MiB |
1.1.3 |
rolling linux/jammy R-4.5 | BioM2_1.1.3.tar.gz |
3.2 MiB |
1.1.3 |
latest source/ R- | BioM2_1.1.3.tar.gz |
3.2 MiB |
1.1.3 |
latest linux/jammy R-4.5 | BioM2_1.1.3.tar.gz |
3.2 MiB |
1.1.3 |
2026-04-23 source/ R- | BioM2_1.1.3.tar.gz |
3.2 MiB |
1.1.1 |
2025-04-20 source/ R- | BioM2_1.1.1.tar.gz |
3.2 MiB |