ARIEL Federico Damian
Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19
BUGNON, L.A.; RAAD, J.; MERINO, G.A.; YONES, C.; ARIEL, F.; MILONE, D.H.; STEGMAYER, G.
Machine Learning with Applications
Año: 2021 vol. 6
The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible forthe pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach basedon deep learning is proposed for identifying precursors of small active RNA molecules named microRNA(miRNA) in the genome of the novel coronavirus. Viral miRNA-like molecules have shown to modulate the hosttranscriptome during the infection progression, thus their identification is crucial for helping the diagnosis ormedical treatment of the disease. The existence of the mature miRNAs derived from computationally predictedmiRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data fromSARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurateand useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machinelearning in the response to a global sanitary emergency. Moreover, the interpretability of our model shedlight on the molecular mechanisms underlying the viral infection, thus contributing to the fight against theCOVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances inmachine learning can be used, effectively, in response to public health emergencies. The approach developedin this work could be of great help in future similar emergencies to accelerate the understanding of thesingularities of any viral agent and for the development of novel therapies.