An information theoretic approach to assessing Gene-Ontology-driven similarity and its application

Haiying Wang, Francisco Azuaje, Huiru Zheng*

*Corresponding author for this work

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    Using information-theoretic approaches, this paper presents a cross-platform system to support the integration of Gene Ontology (GO)-driven similarity knowledge into functional genomics. Three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Two approaches (simple and highest average similarity) which are based on the aggregation of between-term similarities, are used to estimate the similarity between gene products. The system has been successfully applied to a number of applications including assessing gene expression correlation patterns and the relationships between GO-driven similarity and other functional properties.

    Original languageEnglish
    Pages (from-to)121-134
    Number of pages14
    JournalInternational Journal of Data Mining and Bioinformatics
    Volume9
    Issue number2
    DOIs
    Publication statusPublished - 2014

    Keywords

    • Gene ontology
    • GO
    • Information content
    • Semantic similarity

    Fingerprint

    Dive into the research topics of 'An information theoretic approach to assessing Gene-Ontology-driven similarity and its application'. Together they form a unique fingerprint.

    Cite this