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ArXiv
ArXiv
arxiv
ArXiv
2331-8422
Cornell University

arXiv:2407.11435v2
2407.11435
2
preprint
Article
Genomic Language Models: Opportunities and Challenges
Benegas Gonzalo
Ye Chengzhong
Albors Carlos
Li Jianan Canal
Song Yun S.
22 9 2024
arXiv:2407.11435v216 7 2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://arxiv.org/abs/2407.11435v2
nihpp-2407.11435v2.pdf
Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating gLMs.

Review article; 26 pages, 3 figures, 1 table
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pmc
