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.
