Evidence map›Paper›PMID 28155727›Full record

ArticleBMC systems biology2016

An improved method for functional similarity analysis of genes based on Gene Ontology.

Zhen Tian, Chunyu Wang, Maozu Guo, Xiaoyan Liu, Zhixia Teng

Abstract read
In one paragraph

Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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11citing papers in PubMed
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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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  11. Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy.Database : the journal of biological databases and curation · 2017
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zhen TianDepartment of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Chunyu WangDepartment of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Maozu GuoDepartment of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China. maozuguo@hit.edu.cn.
Xiaoyan LiuDepartment of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Zhixia TengDepartment of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMeasures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem.

resultsWe propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms.

conclusionsThe proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .

Indexed as

Gene OntologyComputational BiologyFungal ProteinsHumansProtein Interaction MappingFungal ProteinsGene functional similarityGene OntologySpecificity of termsWeighted inherited semantics

Identifiers

PMID28155727
PMCPMC5259995

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.