GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2...

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Detalles Bibliográficos
Autores principales: Zvyagin, Maxim, Brace, Alexander, Hippe, Kyle, Deng, Yuntian, Zhang, Bin, Bohorquez, Cindy Orozco, Clyde, Austin, Kale, Bharat, Perez-Rivera, Danilo, Ma, Heng, Mann, Carla M., Irvin, Michael, Pauloski, J. Gregory, Ward, Logan, Hayot-Sasson, Valerie, Emani, Murali, Foreman, Sam, Xie, Zhen, Lin, Diangen, Shukla, Maulik, Nie, Weili, Romero, Josh, Dallago, Christian, Vahdat, Arash, Xiao, Chaowei, Gibbs, Thomas, Foster, Ian, Davis, James J., Papka, Michael E., Brettin, Thomas, Stevens, Rick, Anandkumar, Anima, Vishwanath, Venkatram, Ramanathan, Arvind
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709791/
https://www.ncbi.nlm.nih.gov/pubmed/36451881
http://dx.doi.org/10.1101/2022.10.10.511571

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709791/
https://www.ncbi.nlm.nih.gov/pubmed/36451881
http://dx.doi.org/10.1101/2022.10.10.511571

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