Integration of Gene Ontology-based similarities for supporting analysis of protein-protein interaction networks

Haiying Wang*, Huiru Zheng, Fiona Browne, David H. Glass, Francisco Azuaje

*Corresponding author for this work

    Research output: Contribution to journalArticleResearchpeer-review

    12 Citations (Scopus)

    Abstract

    In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein-protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein-protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.

    Original languageEnglish
    Pages (from-to)2073-2082
    Number of pages10
    JournalPattern Recognition Letters
    Volume31
    Issue number14
    DOIs
    Publication statusPublished - 15 Oct 2010

    Keywords

    • Bayesian networks
    • Classification
    • Gene ontology
    • Protein interaction networks

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