-
Tytuł:
-
A novel scheme for essential protein discovery based on multi-source biological information.
-
Autorzy:
-
Liu W; College of Information Engineering of Yangzhou University, Yangzhou 225127, China; The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaiyin 223002, China; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea. Electronic address: .
Ma L; College of Information Engineering of Yangzhou University, Yangzhou 225127, China.
Chen L; College of Information Engineering of Yangzhou University, Yangzhou 225127, China.
Chen B; The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaiyin 223002, China.
Jeon B; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea.
Qiang J; College of Information Engineering of Yangzhou University, Yangzhou 225127, China.
-
Źródło:
-
Journal of theoretical biology [J Theor Biol] 2020 Nov 07; Vol. 504, pp. 110414. Date of Electronic Publication: 2020 Jul 23.
-
Typ publikacji:
-
Journal Article; Research Support, Non-U.S. Gov't
-
Język:
-
English
-
Imprint Name(s):
-
Publication: Amsterdam : Elsevier
Original Publication: London.
-
MeSH Terms:
-
Protein Interaction Mapping*
Proteins*/metabolism
Algorithms ; Computational Biology ; Protein Interaction Maps ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism ; Transcriptome
-
Contributed Indexing:
-
Keywords: Biological information; Essential protein; PPI network
-
Substance Nomenclature:
-
0 (Proteins)
-
Entry Date(s):
-
Date Created: 20200727 Date Completed: 20210621 Latest Revision: 20210621
-
Update Code:
-
20240105
-
DOI:
-
10.1016/j.jtbi.2020.110414
-
PMID:
-
32712150
-
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.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
