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

Author:

Xu Yan1,Zhang Yu-Hang2,Li JiaRui1,Pan Xiao Y.3,Huang Tao2,Cai Yu-Dong1

Affiliation:

1. School of Life Sciences, Shanghai University, Shanghai 200444, China

2. Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

3. BASF & IDLab, Ghent University, Ghent, Belgium

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. Method 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.

Funder

Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences

Youth Innovation Promotion Association of Chinese Academy of Sciences

Shanghai Municipal Science and Technology Major Project

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Bentham Science Publishers Ltd.

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine

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