TY - JOUR
T1 - Integrative network analysis to identify aberrant pathway networks in ovarian cancer
AU - Chen, Li
AU - Xuan, Jianhua
AU - Gu, Jinghua
AU - Wang, Yue
AU - Zhang, Zhen
AU - Wang, Tian Li
AU - Shih, Ie Ming
PY - 2012
Y1 - 2012
N2 - Ovarian cancer is often called the silent killer since it is difficult to have early detection and prognosis. Understanding the biological mechanism related to ovarian cancer becomes extremely important for the purpose of treatment. We propose an integrative framework to identify pathway related networks based on large-scale TCGA copy number data and gene expression profiles. The integrative approach first detects highly conserved copy number altered genes and regards them as seed genes, and then applies a network-based method to identify subnetworks that can differentiate gene expression patterns between different phenotypes of ovarian cancer patients. The identified subnetworks are further validated on an independent gene expression data set using a network-based classification method. The experimental results show that our approach can not only achieve good prediction performance across different data sets, but also identify biological meaningful subnetworks involved in many signaling pathways related to ovarian cancer.
AB - Ovarian cancer is often called the silent killer since it is difficult to have early detection and prognosis. Understanding the biological mechanism related to ovarian cancer becomes extremely important for the purpose of treatment. We propose an integrative framework to identify pathway related networks based on large-scale TCGA copy number data and gene expression profiles. The integrative approach first detects highly conserved copy number altered genes and regards them as seed genes, and then applies a network-based method to identify subnetworks that can differentiate gene expression patterns between different phenotypes of ovarian cancer patients. The identified subnetworks are further validated on an independent gene expression data set using a network-based classification method. The experimental results show that our approach can not only achieve good prediction performance across different data sets, but also identify biological meaningful subnetworks involved in many signaling pathways related to ovarian cancer.
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M3 - Conference article
C2 - 22174260
AN - SCOPUS:84891456090
SN - 2335-6936
SP - 31
EP - 42
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
T2 - 17th Pacific Symposium on Biocomputing, PSB 2012
Y2 - 3 January 2012 through 7 January 2012
ER -