Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Abstract

The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible for the pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach based on 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 host transcriptome during the infection progression, thus their identification is crucial for helping the diagnosis or medical treatment of the disease. The existence of the mature miRNAs derived from computationally predicted miRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data from SARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurate and useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machine learning in the response to a global sanitary emergency. Moreover, the interpretability of our model shed light on the molecular mechanisms underlying the viral infection, thus contributing to the fight against the COVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances in machine learning can be used, effectively, in response to public health emergencies. The approach developed in this work could be of great help in future similar emergencies to accelerate the understanding of the singularities of any viral agent and for the development of novel therapies. Data and source code available at : https://sourceforge.net/projects/sourcesinc/files/aicovid/ .

Robust and efficient COVID-19 detection techniques: A machine learning approach

“…Table 3 shows the comparison of RUNN-COV against various approaches in literature. Among them, the only one that used the same dataset achieved an accuracy of 51% [ 57 ] while RUNN-COV achieves 98.26% accuracy. RUNN-COV also has comparable results to other models that were run on different datasets.…”

“…This problem of class imbalance in SARS-CoV-2 pre-miRNA detection was demonstrated using various algorithms such as one-class SVM (OC-SVM), deeSOM, and mirDNN [ 57 ]. The imbalance ratio in the dataset was varied from 1:50 to 1:200, with decreasing performance as imbalance ratio increased.…”

“…The dataset was used based upon pre-miRNA detection using machine learning techniques (Bugnon et al, 2021). It was derived by applying various techniques the SARS-CoV-2 genome from National Center for Biotechnology Information (NCBI) Reference Sequence NC_045512.2, resulting in 569 pre-miRNA samples with 73 features.…”

Host cell entry mediators implicated in the cellular tropism of SARS‑CoV‑2, the pathophysiology of COVID‑19 and the identification of microRNAs that can modulate the expression of these mediators (Review)

“…Several miRNAs have been either predicted or shown to target SARS-CoV-2 RNAs ( 61 ). Indeed, a number of miRNAs have been found to target the RNA S glycoprotein sequence of SARS-CoV-2, which interacts with ACE2 for viral entry into host cells, including hsa-miR-4661-3p ( 62 ), hsa-miR-510-3p, hsa-miR-624-5p, hsa-miR-497-5p ( 63 ), hsa-miR-622, hsa-miR-761, miR-A3r, hsa-miR-15b-5p, miR-A2r, hsa-miR-196a-5p ( 64 ), and miR-338-3p, miR-4661-3p, miR-4761-5p, hsa-miR-4464, hsa-miR-1234-3p, hsa-miR-7107-5p and hsa-miR-885-5p, which have been shown to bind to the receptor binding domain of the S gene ( 62 ).…”

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

“…In addition, the discovery of SARS-CoV-2 encoded miRNAs that can target human genes has also been investigated, although it is controversial because RNA viruses are mainly replicated in the cytoplasm and miRNA production may interfere with the replication of the viral genome. Several machine-learning-based bioinformatics tools and databases have been developed to predict virus-encoded miRNA and possible targets of human genes [ 16 , 17 , 18 ].…”