Gene set selection via LASSO penalized regression (SLPR).

ABSTRACT: Gene set testing is an important bioinformatics technique that addresses the challenges of power, interpretation and replication. To better support the analysis of large and highly overlapping gene set collections, researchers have recently developed a number of multiset methods that jointly evaluate all gene sets in a collection to identify a parsimonious group of functionally independent sets. Unfortunately, current multiset methods all use binary indicators for gene and gene set activity and assume that a gene is active if any containing gene set is active. This simplistic model limits performance on many types of genomic data. To address this limitation, we developed gene set Selection via LASSO Penalized Regression (SLPR), a novel mapping of multiset gene set testing to penalized mult

SUBMITTER: Frost HR 

PROVIDER: S-EPMC5499546 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature