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

Volume: 13, Issue: 2, Pages: 55 - 68
Published: Apr 1, 2019
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...
Paper Details
Title
Identification of self-regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data
Published Date
Apr 1, 2019
Volume
13
Issue
2
Pages
55 - 68
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