Abstract:
Viral escape analysis has become significantly important area of research due to an unpredictable nature of frequent gene mutations in viruses. Mutation in genes of virus...Show MoreMetadata
Abstract:
Viral escape analysis has become significantly important area of research due to an unpredictable nature of frequent gene mutations in viruses. Mutation in genes of virus is posing frequent threats to humanity from time to time as the drug or vaccines developed for virus becomes less effective against the mutated viruses. In this study, we proposed a novel method for identifying significant mutational viral sequences from non-significant mutational sequences using the escape mutant information of the SARS-COV-2 viral sequences with supervised transformer based binary classification technique. With our novel approach, we achieved model classification; validation and test accuracy of 0.983, 0.9014 respectively on the split size of 0.7-0.15-0.15 used for train, validation, and test dataset respectively. The developed model would be helpful for computationally identifying the significant mutational sequences without in-vivo experiments.
Published in: 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
ISBN Information: