Detection of gene orthology based on protein-protein interaction networks

Fadi Towfic, M. Heather West Greenlee, Vasant Honavar

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

6 Scopus citations

Abstract

Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families. We exploit features derived from the alignment of protein-protein interaction networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository for protein-protein interaction data using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms. The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Pages48-53
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 - Washington, D.C., United States
Duration: Nov 1 2009Nov 4 2009

Publication series

Name2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

Other

Other2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Country/TerritoryUnited States
CityWashington, D.C.
Period11/1/0911/4/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Biomedical Engineering
  • Health Informatics

Fingerprint

Dive into the research topics of 'Detection of gene orthology based on protein-protein interaction networks'. Together they form a unique fingerprint.

Cite this