ArticleNature methods2025
Nucleotide Transformer: building and evaluating robust foundation models for human genomics.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 338 papers, 1 of them a synthesis that pooled it.
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Who cites it
338 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- AI foundation models for RNA biology.RNA biology · 2026Review
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features.PLoS computational biology · 2026Article
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- GenART: Reading the genome's language with adaptive "words".Cell reports methods · 2026Article
- Multi-omics integration and artificial intelligence for the conservation and utilization of local chicken genetic resources.Poultry science · 2026Review
- An atlas of transcription factor cooperation reveals how motif readers shape regulatory output.bioRxiv : the preprint server for biology · 2026Article
- Astronomical spectra as language: Order-agnostic foundation model for low-SNR reconstruction and stellar parameter prediction.iScience · 2026Article
- Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.Genome biology · 2026Article
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- Article
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Review
- Review
- A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.Scientific reports · 2026Article
- Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.NAR genomics and bioinformatics · 2026Article
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
278 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The prediction of molecular phenotypes from DNA sequences remains a longstanding challenge in genomics, often driven by limited annotated data and the inability to transfer learnings between tasks. Here, we present an extensive study of foundation models pre-trained on DNA sequences, named Nucleotide Transformer, ranging from 50 million up to 2.5 billion parameters and integrating information from 3,202 human genomes and 850 genomes from diverse species. These transformer models yield context-specific representations of nucleotide sequences, which allow for accurate predictions even in low-data settings. We show that the developed models can be fine-tuned at low cost to solve a variety of genomics applications. Despite no supervision, the models learned to focus attention on key genomic elements and can be used to improve the prioritization of genetic variants. The training and application of foundational models in genomics provides a widely applicable approach for accurate molecular phenotype prediction from DNA sequence.
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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.