PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning.

ABSTRACT: One of the challenges in a viral pandemic is the emergence of novel variants with different phenotypical characteristics. An ability to forecast future viral individuals at the sequence level enables advance preparation by characterizing the sequences and closing vulnerabilities in current preventative and therapeutic methods. In this article, we explore, in the context of a viral pandemic, the problem of generating complete instances of undiscovered viral protein sequences, which have a high likelihood of being discovered in the future using protein language models. Current approaches to training these models fit model parameters to a known sequence set, which does not suit pandemic forecasting as future sequences differ from known sequences in some respects. To address this, we develop a

SUBMITTER: Ramachandran A 

PROVIDER: S-EPMC10829978 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature