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Comb Chem High Throughput Screen. 2019 Nov 28. doi: 10.2174/1386207322666191129114741. [Epub ahead of print]

New computational tool based on machine-learning algorithms for the identification of rhinovirus infection-related genes.

Author information

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.

KEYWORDS:

Human rhinovirus; Incremental feature selection; Maximum relevance minimum redundancy; OTOF; SOCS1; Support vector machine

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