New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes.

JiaRui Li, Yan Xu, Yu-Hang Zhang, Xiao Y Pan, Tao Huang, Yu-Dong Cai

Journal: Combinatorial chemistry & high throughput screening 2020;22(10):665-674

PMID: 31782358

Abstract

BACKGROUND

Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discovered to be related to rhinovirus infection. However, the pathogenic mechanism of rhinovirus is difficult to elucidate through experimental approaches due to the high cost and consuming time.

METHODS AND RESULTS

In this study, we presented a novel approach that relies on machine-learning algorithms and identified two genes OTOF and SOCS1. The expression levels of these genes in the blood samples can be used to accurately distinguish virus-infected and non-infected individuals.

CONCLUSION

Our findings suggest the crucial roles of these two genes in rhinovirus infection and the robustness of the computational tool in dissecting pathogenic mechanisms.

Address: School of Life Sciences, Shanghai University, Shanghai 200444, China.; Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.; BASF & IDLab, Ghent University, Ghent, Belgium.

DOI: 10.2174/1386207322666191129114741

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MeSH Terms: Computational Biology, Gene Expression Profiling, Humans, Membrane Proteins, Picornaviridae Infections, Support Vector Machine, Suppressor of Cytokine Signaling 1 Protein