A novel scheme for essential protein discovery based on multi-source biological information.
Biological information
Essential protein
PPI network
Journal
Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342
Informations de publication
Date de publication:
07 11 2020
07 11 2020
Historique:
received:
24
06
2019
revised:
14
02
2020
accepted:
15
07
2020
pubmed:
28
7
2020
medline:
22
6
2021
entrez:
27
7
2020
Statut:
ppublish
Résumé
Mining essential protein is crucial for discovering the process of cellular organization and viability. At present, there are many computational methods for essential proteins detecting. However, these existing methods only focus on the topological information of the networks and ignore the biological information of proteins, which lead to low accuracy of essential protein identification. Therefore, this paper presents a new essential proteins prediction strategy, called DEP-MSB which integrates a variety of biological information including gene expression profiles, GO annotations, and Domain interaction strength. In order to evaluate the performance of DEP-MSB, we conduct a series of experiments on the yeast PPI network and the experimental results have shown that the proposed algorithm DEP-MSB is more superior to the other existing traditional methods and has obviously improvement in prediction accuracy.
Identifiants
pubmed: 32712150
pii: S0022-5193(20)30269-1
doi: 10.1016/j.jtbi.2020.110414
pii:
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
110414Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.