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Improving the Measurement of Semantic Similarity between Gene Ontology Terms and Gene Products: Insights from an Edge- and IC-Based Hybrid Method

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  • Xiaomei Wu
  • Erli Pang
  • Kui Lin
  • Zhen-Ming Pei

Abstract

Background: Explicit comparisons based on the semantic similarity of Gene Ontology terms provide a quantitative way to measure the functional similarity between gene products and are widely applied in large-scale genomic research via integration with other models. Previously, we presented an edge-based method, Relative Specificity Similarity (RSS), which takes the global position of relevant terms into account. However, edge-based semantic similarity metrics are sensitive to the intrinsic structure of GO and simply consider terms at the same level in the ontology to be equally specific nodes, revealing the weaknesses that could be complemented using information content (IC). Results and Conclusions: Here, we used the IC-based nodes to improve RSS and proposed a new method, Hybrid Relative Specificity Similarity (HRSS). HRSS outperformed other methods in distinguishing true protein-protein interactions from false. HRSS values were divided into four different levels of confidence for protein interactions. In addition, HRSS was statistically the best at obtaining the highest average functional similarity among human-mouse orthologs. Both HRSS and the groupwise measure, simGIC, are superior in correlation with sequence and Pfam similarities. Because different measures are best suited for different circumstances, we compared two pairwise strategies, the maximum and the best-match average, in the evaluation. The former was more effective at inferring physical protein-protein interactions, and the latter at estimating the functional conservation of orthologs and analyzing the CESSM datasets. In conclusion, HRSS can be applied to different biological problems by quantifying the functional similarity between gene products. The algorithm HRSS was implemented in the C programming language, which is freely available from http://cmb.bnu.edu.cn/hrss.

Suggested Citation

  • Xiaomei Wu & Erli Pang & Kui Lin & Zhen-Ming Pei, 2013. "Improving the Measurement of Semantic Similarity between Gene Ontology Terms and Gene Products: Insights from an Edge- and IC-Based Hybrid Method," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0066745
    DOI: 10.1371/journal.pone.0066745
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    References listed on IDEAS

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    1. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
    2. Won-Ki Huh & James V. Falvo & Luke C. Gerke & Adam S. Carroll & Russell W. Howson & Jonathan S. Weissman & Erin K. O'Shea, 2003. "Global analysis of protein localization in budding yeast," Nature, Nature, vol. 425(6959), pages 686-691, October.
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    Cited by:

    1. Gaston K Mazandu & Nicola J Mulder, 2014. "Information Content-Based Gene Ontology Functional Similarity Measures: Which One to Use for a Given Biological Data Type?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-20, December.
    2. Charles Bettembourg & Christian Diot & Olivier Dameron, 2015. "Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
    3. Issa Atoum, 2019. "Scaled Pearson’s Correlation Coefficient for Evaluating Text Similarity Measures," Modern Applied Science, Canadian Center of Science and Education, vol. 13(10), pages 1-26, October.

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