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Open Access Open Badges Methodology article

EgoNet: identification of human disease ego-network modules

Rendong Yang12, Yun Bai3, Zhaohui Qin1 and Tianwei Yu1*

Author Affiliations

1 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd, N.E, Atlanta, GA, USA

2 Current address: Minnesota Supercomputing Institute for Advanced Computational Research (MSI), University of Minnesota, Minneapolis, MN, USA

3 Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA

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BMC Genomics 2014, 15:314  doi:10.1186/1471-2164-15-314

Published: 28 April 2014



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.


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.


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.

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