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

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,...

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Bibliographic Details
Published in:PloS one Vol. 8; no. 5; p. e66745
Main Authors: Wu, Xiaomei, Pang, Erli, Lin, Kui, Pei, Zhen-Ming
Format: Journal Article
Language:English
Published: United States Public Library of Science 31.05.2013
Public Library of Science (PLoS)
Subjects:
Algorithms
Animal models
Animals
Annotations
Biodiversity
Bioinformatics
Biology
C (programming language)
Computational Biology - methods
Computer Simulation
Confidence intervals
Conservation
Correlation analysis
Databases, Genetic
Gene expression
Gene Ontology
Genomes
Genomics - methods
Humans
Internet
Laboratories
Life sciences
Maximum strategies
Methods
Mice
Molecular Sequence Annotation - methods
Ontology
Programming languages
Protein interaction
Protein Interaction Maps
Proteins
ROC Curve
Semantics
Similarity
Similarity measures
Beijing China
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary: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). 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.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: XW. Performed the experiments: XW. Analyzed the data: XW EP KL ZMP. Contributed reagents/materials/analysis tools: XW EP. Wrote the paper: XW.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0066745