ArticleProceedings of the National Academy of Sciences of the United States of America2023
DNA language models are powerful predictors of genome-wide variant effects.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
What it found
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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.
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Who cites it
93 citing papers in PubMed, 116 citations in OpenAlex.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
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- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Article
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- What has population genomics told us about the dynamics of selection and plant adaptation?Molecular biology and evolution · 2026Review
- Genomic constraint and hypervariability correlate with fitness effects in elite tetraploid potato breeding material.PLoS genetics · 2026Article
- Systematic Benchmarking of DNA Sequence Encoding Strategies for Predicting Regulatory Effects of Non-Coding SNPs.International journal of molecular sciences · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Evaluating DNA Function Understanding in Genomic Language Models Using Evolutionarily Implausible Sequences.ACS synthetic biology · 2026Article
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- Decoding the enhancer-driven regulatory network for lactoferrin across mammalian species.Zoological research · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.bioRxiv : the preprint server for biology · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- A conversational multi-agent AI system for automated plant phenotyping.Nature communications · 2026Article
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- Identification of Genetic Variation Associated With Heat Tolerance in Cowpea (Vigna unguiculata L. Walp.).Molecular ecology · 2026Article
- Massively parallel promoter mapping identifies edits to boost sorghum gene expression.Nature biotechnology · 2026Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN), a model designed to learn genome-wide variant effects through unsupervised pretraining on genomic DNA sequences. Our model also successfully learns gene structure and DNA motifs without any supervision. To demonstrate its utility, we train GPN on unaligned reference genomes of
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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.