Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning.

ABSTRACT: The high spread rate of SARS-CoV-2 virus has put the researchers all over the world in a demanding situation. The need of the hour is to develop novel learning algorithms that can effectively learn a general pattern by training with fewer genome sequences of coronavirus. Learning from very few training samples is necessary and important during the beginning of a disease outbreak when sequencing data is limited. This is because a successful detection and isolation of patients can curb the spread of the virus. However, this poses a huge challenge for machine learning and deep learning algorithms as they require huge amounts of training data to learn the pattern and distinguish from other closely related viruses. In this paper, we propose a new paradigm - Neurochaos Learning (NL) for classifi

SUBMITTER: Harikrishnan NB 

PROVIDER: S-EPMC9170350 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature