Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.

ABSTRACT: Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsi

SUBMITTER: Kontio JAJ 

PROVIDER: S-EPMC7337083 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature