EgoNet: identification of human disease ego-network modules

BMC Genomics

Tianwei Yu¹  Zhaohui Qin¹  Yun Bai³  Rendong Yang² 

[1] Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd, N.E, Atlanta, GA, USA;Current address: Minnesota Supercomputing Institute for Advanced Computational Research (MSI), University of Minnesota, Minneapolis, MN, USA;Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA

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

Others  :  1217400
DOI  :  10.1186/1471-2164-15-314

 received in 2013-12-10, accepted in 2014-04-16,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Yang et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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