ReviewTrends in genetics : TIG2025
Genomic language models: opportunities and challenges.
Review in Trends in genetics : TIG, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed.
- Novel genetic profile linked to cognitive decline in Hispanics/Latinos.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Article
- A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction.bioRxiv : the preprint server for biology · 2026Article
- Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.Veterinary and animal science · 2026Review
- Accelerating inference in genomic and proteomic foundation models via speculative decoding.Bioinformatics (Oxford, England) · 2026Article
- Using the DNA language model, GROVER, to parse effects of sequence, chromatin and regulatory features on genome stability.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Advances in Decoding Bacterial N-Terminal Proteoforms: Technologies, Challenges, and Functional Insights.Microorganisms · 2026Review
- ATACompass enables cross-species cell identity mapping for scATAC-seq without gene annotations.Cell reports methods · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Evaluating DNA Function Understanding in Genomic Language Models Using Evolutionarily Implausible Sequences.ACS synthetic biology · 2026Article
- Article
- KEGG orthology-based machine learning reveals functional determinants of antimicrobial resistance inMicrobiology spectrum · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
39753409What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.