Exploring coronavirus sequence motifs through convolutional neural network for accurate identification of COVID-19.

Abstract

The SARS-CoV-2 virus reportedly originated in Wuhan in 2019, causing the coronavirus outbreak (COVID-19), which was technically designated as a global epidemic. Numerous studies have been carried out to diagnose and treat COVID-19 throughout the midst of the disease's spread. However, the genetic similarity between COVID-19 and other types of coronaviruses makes it challenging to differentiate between them. Therefore it's essential to swiftly identify if an epidemic is brought on by a brand-new virus or a well-known disease. In the present article, the DeepCoV deep-learning (DL) approach utilizes layered convolutional neural networks (CNNs) to classify viral serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) besides other viral diseases. Additionally, various motifs linked with SARS-CoV-2 can be located by examining the computational filter processes. In identifying these important motifs, DeepCoV reveals the transparency of CNNs. Experiments were conducted using the 2019nCoVR datasets, and the results indicate that DeepCoV performed more accurately than several benchmark ML models. Additionally, DeepCoV scored its maximum area under the precision-recall curve (AUCPR) and receiver operating characteristic curve (AUC-ROC) at 98.62% and 98.58%, respectively. Overall, these investigations provide strong knowledge of the employment of deep learning (DL) algorithms as a crucial alternative to identifying SARS-CoV-2 and identifying patterns of disease in the SARS-CoV-2 genes.