Pith. sign in

REVIEW 1 cited by

Genomic Language Models: Opportunities and Challenges

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.11435 v2 pith:HEY22QA4 submitted 2024-07-16 q-bio.GN cs.LGstat.ML

classification q-bio.GNcs.LGstat.ML
keywords glmslanguagemodelssequenceschallengescomplexdevelopinggenomes
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Find Central Dogma Again: Leveraging Multilingual Transfer in Large Language Models

    q-bio.GN 2025-02 reject novelty 5.0 of 10

    A GPT-2 model fine-tuned on multilingual sentence similarity achieves at best 81% accuracy on classifying matching vs non-matching DNA-protein pairs, but the result is highly seed-dependent and only with an easy test set.

Pith tools