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ABSTRACT:
SUBMITTER: Benegas G
PROVIDER: S-EPMC10592768 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature

Benegas Gonzalo G Albors Carlos C Aw Alan J AJ Ye Chengzhong C Song Yun S YS
bioRxiv : the preprint server for biology 20240406
Whereas protein language models have demonstrated remarkable efficacy in predicting the effects of missense variants, DNA counterparts have not yet achieved a similar competitive edge for genome-wide variant effect predictions, especially in complex genomes such as that of humans. To address this challenge, we here introduce GPN-MSA, a novel framework for DNA language models that leverages whole-genome sequence alignments across multiple species and takes only a few hours to train. Across severa ...[more]