Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network

Author(s): HuanHuan Cao, YuHang Zhang, Jia Zhao, Liucun Zhu, Yi Wang, JiaRui Li*, Yuan-Ming Feng*, Ning Zhang*

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 20 , Issue 7 , 2017

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Background: Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous.

Objective: According to the “guilt by association” (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm.

Conclusion: We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes.

Keywords: Ebola virus, pathogenic mechanism, shortest path, human protein identification, betweenness, GO enrichment.

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Article Details

Year: 2017
Published on: 09 March, 2017
Page: [638 - 646]
Pages: 9
DOI: 10.2174/1386207320666170310114816
Price: $65

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