Algorithm for Inference of Gene Regulatory Networks in Breast Cancer

Authors

  • Nimrita Koul, Sunilkumar S Manvi

Abstract

Interactions among genes regulate the physiology of a human cell. The influence that genes exercise on activation or deactivation of other genes is actuated through signaling pathways which involve synthesis of many molecular compounds. Such a group of genes which have a directed relation of either activation or deactivation on one another is known as a gene regulatory network. Computational understanding of regulatory networks by analysis of genomic data can help in early detection and diagnosis of many diseases. This paper presents an algorithm based on clustering and conditional mutual information for inference of gene regulatory networks from breast cancer gene expression data set. Our algorithm has been compared with conventional approaches in terms of number of true positive nodes and edges, true negative nodes and edges. We have done clinical validation of the networks obtained by gene ontology analysis and gene pair enrichment analysis. The results show that our approach works better.

Downloads

Published

2020-05-12

Issue

Section

Articles