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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

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

Author(s): Yan Xu, Yu-Hang Zhang, JiaRui Li, Xiao Y. Pan, Tao Huang* and Yu-Dong Cai*

Volume 22, Issue 10, 2019

Page: [665 - 674] Pages: 10

DOI: 10.2174/1386207322666191129114741

Price: $65

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.

Keywords: Human Rhinovirus, maximum relevance minimum redundancy, support vector machine, incremental feature selection, OTOF, SOCS1.

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