GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity

Hyunjin Kim, Sang Min Choi, Sanghyun Park

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-Associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find 'differentially predicted' genes. Through the proposed method, we discovered more prostate cancer-Associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

Original languageEnglish
Pages (from-to)129-146
Number of pages18
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number1
DOIs
Publication statusPublished - 2018 Jan 1

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

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