Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation.

Abstract excerpt

BACKGROUND: Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find...

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