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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| Veröffentlicht in: | The international journal of high performance computing applications 2023-11, Vol.37 (6), p.683-705 |
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| container_title | The international journal of high performance computing applications |
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| creator | 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 Ozgulbas, Defne G. Vassilieva, Natalia Pauloski, James 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 |
| description | 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 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole-genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data. |
| doi_str_mv | 10.1177/10943420231201154 |
| format | Article |
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| ispartof | The international journal of high performance computing applications, 2023-11, Vol.37 (6), p.683-705 |
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| source | SAGE Complete; Alma/SFX Local Collection |
| subjects | Artificial intelligence Evolution Gene sequencing Genomes Graphics processing units Large language models Severe acute respiratory syndrome coronavirus 2 Training Viral diseases |
| title | GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics |
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