Why the study?
Elucidating the pathogenic mechanism of rhinovirus through experimental approaches is difficult due to high cost and time consumption.
A novel machine-learning approach identified OTOF and SOCS1 as key genes that can distinguish rhinovirus-infected from non-infected individuals based on blood expression levels.
Requires experimental validation before clinical consideration; leaves open therapeutic targeting in rhinovirus infection.
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.
No takes yet. Share an insight, caveat, or question.
Xu et al. (2019) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: