Integration of Gene Ontology-based similarities for supporting analysis of protein–protein interaction networks

HY Wang, Huiru Zheng, Fiona Browne, DH Glass, Francisco Azuaje

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein–protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein–protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.
LanguageEnglish
Pages2073-2082
JournalPattern Recognition Letters
Volume31
Issue number14
DOIs
Publication statusPublished - 2010

Fingerprint

Ontology
Genes
Proteins
Bayesian networks
Yeast
Semantics

Keywords

  • Gene ontology
  • Protein interaction networks
  • Bayesian networks
  • Classification

Cite this

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abstract = "In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein–protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein–protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.",
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Integration of Gene Ontology-based similarities for supporting analysis of protein–protein interaction networks. / Wang, HY; Zheng, Huiru; Browne, Fiona; Glass, DH; Azuaje, Francisco.

In: Pattern Recognition Letters, Vol. 31, No. 14, 2010, p. 2073-2082.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Integration of Gene Ontology-based similarities for supporting analysis of protein–protein interaction networks

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AU - Zheng, Huiru

AU - Browne, Fiona

AU - Glass, DH

AU - Azuaje, Francisco

PY - 2010

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AB - In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein–protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein–protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.

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KW - Protein interaction networks

KW - Bayesian networks

KW - Classification

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