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Significance and Functional Similarity for Identification of Disease Genes.

Authors :
Maji, Pradipta
Shah, Ekta
Source :
IEEE/ACM Transactions on Computational Biology & Bioinformatics; Nov/Dec2017, Vol. 14 Issue 6, p1419-1433, 15p
Publication Year :
2017

Abstract

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
15455963
Volume :
14
Issue :
6
Database :
Complementary Index
Journal :
IEEE/ACM Transactions on Computational Biology & Bioinformatics
Publication Type :
Academic Journal
Accession number :
126654043
Full Text :
https://doi.org/10.1109/TCBB.2016.2598163