Detecting protein complexes using gene expression biclusters

Eileen Marie Hanna, Nazar M. Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The importance of detecting protein complexes in protein interaction networks originates from the fact that they are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Despite the notable performance of the current computational methods for detecting protein complexes, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of complexes. In this paper, we present "DyCluster", a framework to model dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores.

Original languageEnglish
Title of host publication2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479969265
DOIs
Publication statusPublished - Oct 16 2015
EventIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015 - Niagara Falls, Canada
Duration: Aug 12 2015Aug 15 2015

Publication series

Name2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015

Other

OtherIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
Country/TerritoryCanada
CityNiagara Falls
Period8/12/158/15/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Health Informatics
  • Artificial Intelligence
  • Biomedical Engineering

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