Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood.

ABSTRACT: Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates. By considering the intrinsic property of the gene data which

SUBMITTER: Chatrabgoun O 

PROVIDER: S-EPMC11778759 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature