Identifying essential proteins in dynamic protein networks based on an improved h-index algorithm.
Journal
BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682
Informations de publication
Date de publication:
17 06 2020
17 06 2020
Historique:
received:
27
10
2019
accepted:
01
06
2020
entrez:
20
6
2020
pubmed:
20
6
2020
medline:
6
1
2021
Statut:
epublish
Résumé
The essential proteins in protein networks play an important role in complex cellular functions and in protein evolution. Therefore, the identification of essential proteins in a network can help to explain the structure, function, and dynamics of basic cellular networks. The existing dynamic protein networks regard the protein components as the same at all time points; however, the role of proteins can vary over time. To improve the accuracy of identifying essential proteins, an improved h-index algorithm based on the attenuation coefficient method is proposed in this paper. This method incorporates previously neglected node information to improve the accuracy of the essential protein search. Based on choosing the appropriate attenuation coefficient, the values, such as monotonicity, SN, SP, PPV and NPV of different essential protein search algorithms are tested. The experimental results show that, the algorithm proposed in this paper can ensure the accuracy of the found proteins while identifying more essential proteins. The described experiments show that this method is more effective than other similar methods in identifying essential proteins in dynamic protein networks. This study can better explain the mechanism of life activities and provide theoretical basis for the research and development of targeted drugs.
Sections du résumé
BACKGROUND
The essential proteins in protein networks play an important role in complex cellular functions and in protein evolution. Therefore, the identification of essential proteins in a network can help to explain the structure, function, and dynamics of basic cellular networks. The existing dynamic protein networks regard the protein components as the same at all time points; however, the role of proteins can vary over time.
METHODS
To improve the accuracy of identifying essential proteins, an improved h-index algorithm based on the attenuation coefficient method is proposed in this paper. This method incorporates previously neglected node information to improve the accuracy of the essential protein search. Based on choosing the appropriate attenuation coefficient, the values, such as monotonicity, SN, SP, PPV and NPV of different essential protein search algorithms are tested.
RESULTS
The experimental results show that, the algorithm proposed in this paper can ensure the accuracy of the found proteins while identifying more essential proteins.
CONCLUSIONS
The described experiments show that this method is more effective than other similar methods in identifying essential proteins in dynamic protein networks. This study can better explain the mechanism of life activities and provide theoretical basis for the research and development of targeted drugs.
Identifiants
pubmed: 32552708
doi: 10.1186/s12911-020-01141-x
pii: 10.1186/s12911-020-01141-x
pmc: PMC7371468
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
110Subventions
Organisme : Natural Science Foundation of Jiangsu Province
ID : BK20180822
Pays : International
Organisme : National Natural Science Foundation of China
ID : 61906100
Pays : International
Organisme : Natural Science Research Projects in Jiangsu Higher Education Institution
ID : 18KJB520040
Pays : International
Références
Nat Commun. 2016 Jan 12;7:10168
pubmed: 26754161
BMC Genomics. 2010 Dec 01;11 Suppl 3:S7
pubmed: 21143789
BMC Bioinformatics. 2015 Jun 16;16:195
pubmed: 26077899
BMC Bioinformatics. 2006 Jun 15;7:302
pubmed: 16776819
J Proteome Res. 2016 Dec 2;15(12):4755-4762
pubmed: 27766879
Biomed Res Int. 2015;2015:839590
pubmed: 25815337
BMC Genomics. 2013;14 Suppl 4:S7
pubmed: 24267033
PLoS One. 2017 Nov 9;12(11):e0187091
pubmed: 29121073
IEEE Trans Nanobioscience. 2014 Dec;13(4):415-24
pubmed: 25122840
PLoS One. 2018 Apr 10;13(4):e0195410
pubmed: 29634727
BMC Syst Biol. 2012 Jul 18;6:87
pubmed: 22808943
Acta Inform Med. 2016 Jul 16;24(4):228-232
pubmed: 27708482
Proteins. 2012 Oct;80(10):2459-68
pubmed: 22685080
Nucleic Acids Res. 2009 Jan;37(Database issue):D455-8
pubmed: 18974178
PLoS One. 2011 Mar 30;6(3):e18258
pubmed: 21483473
BMC Bioinformatics. 2016 Feb 24;17:97
pubmed: 26911432
IEEE/ACM Trans Comput Biol Bioinform. 2018 Jan-Feb;15(1):109-116
pubmed: 28650821
PLoS One. 2015 Jun 30;10(6):e0131418
pubmed: 26125187
Bioinformatics. 2003 Mar 22;19(5):635-42
pubmed: 12651722