NeMoFinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs

Jin Chen, Wynne Hsu, Mong Li Lee, See Kiong Ng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

116 Scopus citations

Abstract

Recent works in network analysis have revealed the existence of network motifs in biological networks such as the protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find mesoscale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes. We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale network motifs that are repeated and unique in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data that were generated via highly erroneous high-throughput experimental methods.

Original languageEnglish
Title of host publicationKDD 2006
Subtitle of host publicationProceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages106-115
Number of pages10
StatePublished - 2006
EventKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States
Duration: Aug 20 2006Aug 23 2006

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2006

Conference

ConferenceKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryUnited States
CityPhiladelphia, PA
Period8/20/068/23/06

Keywords

  • Graph mining
  • Network motif
  • Protein-protein interaction network

ASJC Scopus subject areas

  • Information Systems

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