Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.

Authors

  • Irina M Armean
    Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK.
  • Kathryn S Lilley
    Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK.
  • Matthew W B Trotter
    Celegene Institute for Translational Research Europe (CITRE), Sevilla 41092, Spain.
  • Nicholas C V Pilkington
    Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.
  • Sean B Holden
    Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.