Assessing the impact of network depth on the analysis of PPI networks: A case study

Jaine K. Blayney, Haiying Wang, Huiru Zheng, Francisco Azuaje

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

2 Citations (Scopus)

Abstract

Recent years have seen a growing interest in the incorporation of protein-protein interaction (PPI) networks to support functional genomic research. Often a default depth is assumed by network inference software. This case study considers the impact of network depth on the analysis of PPI networks using seven proteins known to be relevant to heart failure as inputs into the analysis. This paper analyses how the characteristics of a PPI network vary according to the level examined, suggesting that the investigation of network topology is an essential first step in PPI analysis. The classification of nodes, in terms of degree and betweenness centrality, within the network is also considered. The effect of network depth is also proved to be significant in the identification of potentially essential proteins with large connectivity and/or high betweenness centrality values.

Original languageEnglish
Title of host publication2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
PublisherIEEE Computer Society
Pages200-207
Number of pages8
ISBN (Print)9781424427567
DOIs
Publication statusPublished - 01 Jan 2009
Externally publishedYes
Event2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Nashville, TN, United States
Duration: 30 Mar 200902 Apr 2009

Publication series

Name2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings

Conference

Conference2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009
CountryUnited States
CityNashville, TN
Period30/03/200902/04/2009

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence
  • Computational Theory and Mathematics

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