Identifying consistent disease subnetworks using DNet

Jiajie Peng, Junya Lu, Xuequn Shang, Jin Chen

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


It is critical to identify disease-specific subnetworks from the vastly available genome-wide gene expression data for elucidating how genes perform high-level biological functions together. Various algorithms have been developed for disease gene identification. However, the topological structure of the disease networks (or even the fraction of the networks) has been left largely unexplored. In this article, we present DNet, a method for the identification of significant disease subnetworks by integrating both the network structure and gene expression information. Our work will lead to the identification of missing key disease genes, which are be highly expressed in a disease-specific gene expression dataset. The experimental evaluation of our method on both the Leukemia and the Duchenne Muscular Dystrophy gene expression datasets show that DNet performs better than the existing state-of-the-art methods. In addition, literature supports were found for the discovered disease subnetworks in a case study.

Original languageEnglish
Pages (from-to)104-110
Number of pages7
StatePublished - Dec 1 2017

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Inc.


  • Disease network
  • Gene expression
  • Network structure

ASJC Scopus subject areas

  • Molecular Biology
  • General Biochemistry, Genetics and Molecular Biology


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