DiME : a scalable disease module identification algorithm with application to glioma progression

Research output: Contribution to journalArticle

External organisations

  • School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
  • College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.

Abstract

Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.

Details

Original languageEnglish
Article numbere86693
JournalPLoS ONE
Volume9
Issue number2
Publication statusPublished - 11 Feb 2014

Keywords

  • Algorithms, Gene expression, Genetic networks, Glioma, Network analysis, Social networks, Topology, Transcription factors