Uncovering biomarker genes with enriched classification potential from Hallmark gene sets.

ABSTRACT: Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call "candidate genes", by evaluating the ability of gene combinations to classify samples from a dataset, which we call "classification potential". Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecula

SUBMITTER: Targonski CA 

PROVIDER: S-EPMC6611793 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature