Integrative network analysis to identify aberrant pathway networks in ovarian cancer

Li Chen, Jianhua Xuan, Jinghua Gu, Yue Wang, Zhen Zhang, Tian Li Wang, Ie Ming Shih

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)31-42
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - 2012
Event17th Pacific Symposium on Biocomputing, PSB 2012 - Kohala Coast, United States
Duration: Jan 3 2012Jan 7 2012

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Integrative network analysis to identify aberrant pathway networks in ovarian cancer'. Together they form a unique fingerprint.

Cite this