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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