Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis.


Journal

IEEE/ACM transactions on computational biology and bioinformatics
ISSN: 1557-9964
Titre abrégé: IEEE/ACM Trans Comput Biol Bioinform
Pays: United States
ID NLM: 101196755

Informations de publication

Date de publication:
Historique:
pubmed: 6 4 2020
medline: 21 12 2021
entrez: 6 4 2020
Statut: ppublish

Résumé

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Identifiants

pubmed: 32248121
doi: 10.1109/TCBB.2020.2983894
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1026-1034

Auteurs

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Classifications MeSH