Genomic language models could transform medicine but not yet
Micaela Elisa Consens, Ben Li, Anna R. Poetsch, Stephen Gilbert
Abstract
In February 2025, researchers announced Evo2, a genome language model (gLM) trained on over 128,000 genomes, encompassing over 9.3 trillion DNA base pairs 1 . This computational scale matches leading text-based LLMs, representing a significant milestone for genomic AI 2 . Unlike protein language models, which train to understand the 2% of human DNA that is encoded into amino acids and folded into proteins, gLMs train to understand the entire genome 3 . This largely consists of understanding the role of the remaining 98% of human DNA that is non-coding. Non-coding DNA contains crucial regulatory elements that coordinate gene expression across different cell types and developmental stages 4 , and the precise mechanisms governing this regulation are increasingly being unraveled. This field of study is known as regulatory genomics 4 , and gLMs have emerged as promising tools to study it. The introduction of Evo2 represents both important progress for the field and highlights critical questions about what these models learn and how they might be applied. This article examines gLMs in the context of Evo2, highlighting their potential for biological research and medicine while exploring the technical barriers and ethical challenges—from data privacy to dual-use risks—that will shape their clinical future.