EgoNet: Identification of human disease ego-network modules

Rendong Yang, Yun Bai, Zhaohui Qin, Tianwei Yu

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.Conclusions: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases.

Original languageEnglish (US)
Article number314
JournalBMC Genomics
Volume15
Issue number1
DOIs
StatePublished - Apr 28 2014

Keywords

  • Biological networks
  • Cancer biology
  • Gene expression
  • Machine learning
  • Microarray
  • Network medicine

Fingerprint Dive into the research topics of 'EgoNet: Identification of human disease ego-network modules'. Together they form a unique fingerprint.

  • Cite this