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Bio-primed machine learning to enhance discovery of relevant biomarkers
A) Overview of the method. Biomarker discovery analysis associates the dependency of the target gene with a genome-wide omic profile. Information from biological networks, such as protein-protein interaction networks, is integrated into the LASSO regularization, bio-primed model. As a consequence, features that are linked to the target dependency are prioritized during the feature selection resulting in the discovery of relevant biomarkers. B) Stepwise parameterization procedure first optimizes λ then second optimizes the Φ parameter. Using the optimized λ and Φ parameters a final LASSO model is fit, and the resulting coefficients can be inspected to prioritize biologically relevant biomarkers.
Data
Google drive folder with additional results & draft of manuscript: