Identification of self-regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data.

ABSTRACT: Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self-regulatory interactions. The values of gene co-expressions could help in identifying co-regulations among them. The proposed approach aims at computing the differences in variances of co-expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co-variances using principal component analysis (PCA) to predict an asymmetric and non-diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in

SUBMITTER: Khalid M 

PROVIDER: S-EPMC8687352 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature