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

Hyunjin Kim; Sang Min Choi; Sanghyun Park

doi:10.1109/TCBB.2016.2618927

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

Hyunjin Kim, Sang Min Choi, Sanghyun Park

College of Computing

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	129-146
Number of pages	18
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume	15
Issue number	1
DOIs	https://doi.org/10.1109/TCBB.2016.2618927
Publication status	Published - 2018 Jan 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2015R1A2A1A05001845). The authors appreciate Mr. Junsik Kim’s proofreading efforts. Sanghyun Park is the corresponding author.

Publisher Copyright:
© 2004-2012 IEEE.

All Science Journal Classification (ASJC) codes

Biotechnology
Genetics
Applied Mathematics

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TCBB.2016.2618927

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

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.",

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

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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