Title: Concurrent analysis of copy number variation and gene expression: application in paired non-smoking female lung cancer patients

Authors: Pei-Chun Chen; Tzu-Pin Lu; Jung-Chih Chang; Liang-Chuan Lai; Mong-Hsun Tsai; Chuhsing Kate Hsiao; Eric Y. Chuang

Addresses: Department of Statistics and Informatics Science, Providence University, 200, Chung Chi Rd., Taichun, Taiwan ' Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan ' Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan ' Graduate Institute of Physiology, National Taiwan University, 1, Sec. 1, Jen Ai Rd., Taipei, Taiwan ' Institute of Biotechnology, National Taiwan University, 4F, No. 81, Chang-Xing St., Taipei, Taiwan ' Department of Public Health, Research Centre for Gene, Environment, and Human Health, Bioinformatics and Biostatistics Core, Centre of Genomic Medicine, National Taiwan University, 17, Xu Zhou Rd., Taipei, Taiwan ' Graduate Institute of Biomedical Electronics and Bioinformatics, Bioinformatics and Biostatistics Core, Centre of Genomic Medicine, National Taiwan University, 7F, No.2, Xu Zhou Rd., Taipei, Taiwan

Abstract: Recent studies indicate that both genomic alterations and transcriptional dysregulation influence the disease progresses. This study proposes a method identifying pathways by integrating copy numbers (CN), gene expressions (GE) and their correlations. A lung cancer patients dataset with both normal and tumor tissues is utilized to evaluate the performance of the proposed method. To further appraise the predicting abilities of those pathways, these patients are classified by support vector machines. Based on the classification results, pathways integrating CN, GE and their correlations is more informative and biologically meaningful and perform better than pathways obtained by only CN or only GE.

Keywords: copy number variation; gene expression; concurrent analysis; pathways; gene set enrichment analysis; support vector machines; SVM; non-smoking patients; female patients; lung cancer; bioinformatics.

DOI: 10.1504/IJDMB.2013.054699

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.1, pp.92 - 104

Received: 13 Oct 2011
Accepted: 13 Oct 2011

Published online: 20 Oct 2014 *

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