Title: 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

Addresses: 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

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.

Keywords: gene ranking; text mining; gene relation extraction; disease gene markers; microarray data analysis; GeneRank; gene relation networks; prostate cancer; bioinformatics.

DOI: 10.1504/IJDMB.2012.049250

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.3, pp.239 - 254

Received: 04 May 2011
Accepted: 04 May 2011
Published online: 17 Dec 2014 *

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