Applying Deep Learning for Genome Detection of Coronavirus [post]

Geeta Rani, Meet Ganpatlal Oza, Vijaypal Singh Dhaka, Nitesh Pradhan, Sahil Verma, Joel J. P. C. Rodrigues
2020 unpublished
Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientists are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning model for finding the degree of similarity of the genome of the Severe Acute Respiratory Syndrome-Coronavirus 2 ('SARS-CoV-2') with a given genome. This research also aims at detecting the genome of 'SARS-CoV-2' in the host human
more » ... in the host human beings. The experimental results on the dataset publicly available at National Centre for Biotechnology Information, show that the model is effective in predicting the similarity score of the genomic sequence of 'SARS-CoV-2' and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus, and Human T- cell Leukaemia Virus. This is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27%. It may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.
doi:10.21203/rs.3.rs-93564/v1 fatcat:fyz4lfvmvnfbjjp6y2sfhfjd2a