An Integrated Information-Based Similarity Measurement of Gene Ontology Terms

Shu-Bo Zhang1 and Jian-Huang Lai2

  1. Department of Computer Science, Guangzhou Maritime Institute
    510700 Guangzhou, P.R. China
    845996912@qq.com
  2. School of Information Science and Technology,Sun Yat-sen University
    510275 Guangzhou, P.R. China
    stsljh@mail.sysu.edu.cn

Abstract

Measuring the semantic similarity between pairs of terms in Gene Ontology (GO) can help to compare genes that can not be compared by other computational methods. In this study, we proposed an integrated information-based similarity measurement (IISM) to calculate the semantic similarity between two GO terms by taking into account multiple common ancestors that they share, and aggregating the semantic information and depth information of the non-redundant common ancestors. Our method searches for non-redundant common ancestors in an effective way. Validation experiments were conducted on both gene expression dataset and pathway dataset, and the experimental results suggest the superiority of our method against some existing methods.

Key words

Gene Ontology, GO terms, semantic similarity, biological pathways, gene expression profile

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS141130053Z

Publication information

Volume 12, Issue 4 (November 2015)
Special Issue on Recent Advances in Information Processing, Parallel and Distributed Computing
Year of Publication: 2015
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Zhang, S., Lai, J.: An Integrated Information-Based Similarity Measurement of Gene Ontology Terms. Computer Science and Information Systems, Vol. 12, No. 4, 1235–1253. (2015)