Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients
Journal
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages
599-602
ISBN
978-1424483075
Date Issued
2010
Author(s)
Chang, J.-C.
Chuang, E.Y.
Abstract
This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further assigned a score to incorporate the information from CN, GE, and their correlations. A dataset of 44 female non-smoking lung cancer patients with both normal and tumor tissues was used to evaluate the performance of this method. To further appraise the predicting abilities of those pathways, patients were classified by support vector machines using the pathways identified by only copy number, only gene expression and incorporating CN, GE, and their correlations. The results showed that the proposed method earned higher accuracy, sensitivity and specificity than traditional methods. ©2010 IEEE.
SDGs
Other Subjects
Copy number; Data sets; Lung Cancer; Sensitivity and specificity; Tumor tissues; Window Size; Bioinformatics; Biological organs; Data processing; Diseases; Rating; Gene expression
Type
conference paper