Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway.


Journal

BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173

Informations de publication

Date de publication:
2019
Historique:
received: 04 01 2019
revised: 04 06 2019
accepted: 26 06 2019
entrez: 10 8 2019
pubmed: 10 8 2019
medline: 16 1 2020
Statut: epublish

Résumé

Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein-protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein-protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.

Identifiants

pubmed: 31396531
doi: 10.1155/2019/4963289
pmc: PMC6668538
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4963289

Déclaration de conflit d'intérêts

The authors declare that there are no conflicts of interest regarding the publication of this paper.

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Auteurs

Fei Yuan (F)

Department of Science & Technology, Binzhou Medical University Hospital, Binzhou 256603, Shandong, China.

Xiaoyong Pan (X)

BASF & IDLab, Ghent University, Ghent, Belgium.

Lei Chen (L)

College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China.

Yu-Hang Zhang (YH)

Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Tao Huang (T)

Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Yu-Dong Cai (YD)

School of Life Sciences, Shanghai University, Shanghai 200444, China.

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Classifications MeSH