Chen, P.-C.P.-C.ChenTZU-PIN LUChang, J.-C.J.-C.ChangLIANG-CHUAN LAIMONG-HSUN TSAICHUHSING KATE HSIAOERIC YAO-YU CHUANG2022-04-202022-04-20201317485673https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879679944&doi=10.1504%2fIJDMB.2013.054699&partnerID=40&md5=6bead200830b94572680dd41fbd62c2dhttps://scholars.lib.ntu.edu.tw/handle/123456789/604935Recent 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. Copyright © 2013 Inderscience Enterprises Ltd.[SDGs]SDG3CN; Concurrent analysis; Copy number; GE; Gene expression; Gene set enrichment analysis; Pathways; Support vector machinejournal article10.1504/IJDMB.2013.0546992-s2.0-84879679944