A hybrid approach to gene ranking using gene relation networks derived from literature for the identification of disease gene markers

Authors:
Miyoung Shin;Hyungmin Lee;Munpyo Hong
Affiliations:
Bio-Intelligence Mining Lab., College of IT Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea;Graduate School of EECS, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea;College of Liberal Arts, Sungkyunkwan University, Myungryun-dong 3-ga, Jongro-gu, Seoul 110-745, Korea
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

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Abstract

For the identification of gene markers involved in diseases, microarray expression profiles have been widely used to prioritize genes. In this paper, we propose a novel approach to gene ranking that employs gene relation network derived from literature along with microarray expression scores to calculate ranking statistics of individual genes. In particular, the gene relation network is constructed from literature by applying syntactic analysis and co-occurrence method in a hybrid manner. For evaluation, the proposed method was tested with publicly available prostate cancer data. The result shows that our method is superior to other existing approaches.