TY - GEN
T1 - Concurrent analysis of copy number variation and gene expression
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
AU - Chang, Jung Chih
AU - Lai, Liang Chuan
AU - Lu, Tzu Pin
AU - Tsai, Mong Hsun
AU - Chuang, Eric Y.
AU - Hsiao, Chuhsing Kate
AU - Chen, Pei Chun
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79952379406&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2010.5706636
DO - 10.1109/BIBM.2010.5706636
M3 - 会议稿件
AN - SCOPUS:79952379406
SN - 9781424483075
T3 - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
SP - 599
EP - 602
BT - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Y2 - 18 December 2010 through 21 December 2010
ER -