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

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

Volume: 20 Issue: 7

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

Affiliation:

• School of Life Science, Shanghai University, Shanghai,China

• Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin,China

• Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin,China

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

Abstract: 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.

Cite this article as:

Cao HuanHuan, Zhang YuHang, Zhao Jia , Zhu Liucun , Wang Yi, Li JiaRui *, Feng Yuan-Ming *, Zhang Ning *, Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network, Combinatorial Chemistry & High Throughput Screening 2017; 20 (7) . https://dx.doi.org/10.2174/1386207320666170310114816