Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
13
12
2018
accepted:
01
07
2019
entrez:
24
7
2019
pubmed:
25
7
2019
medline:
19
2
2020
Statut:
epublish
Résumé
Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other protein-protein interaction network-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.
Identifiants
pubmed: 31335894
doi: 10.1371/journal.pone.0209958
pii: PONE-D-18-35634
pmc: PMC6650051
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0209958Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L002817/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L020505/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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