BMRF-Net: A software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method

Xu Shi, Robert O. Barnes, Li Chen, Ayesha N. Shajahan-Haq, Leena Hilakivi-Clarke, Robert Clarke, Yue Wang, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence.

Original languageEnglish (US)
Pages (from-to)2412-2414
Number of pages3
JournalBioinformatics
Volume31
Issue number14
DOIs
StatePublished - Jul 15 2015

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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