Biologically Explainable Machine Learning Framework
Add unmapped probe
Prediction by Machine Learning
Biologically Explainable Machine Learning Framework
Find suitable parameters for partitioning pathways modules
BioM2 Hyperparametric Combination
Delineate differential pathway modules with high biological interpreta...
Correlalogram for Biological Differences Modules
Visualisation of significant pathway-level features
Visualisation Original features that make up the pathway
Network diagram of pathways-level features
Display biological information within each pathway module
Stage 1 Fearture Selection
Stage 2 Fearture Selection
Visualisation of the results of the analysis of the pathway modules
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.