ReviewNature reviews. Genetics2025
Predicting gene expression from DNA sequence using deep learning models.
Review in Nature reviews. Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.RNA biology · 2026Article
- Review
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Genome-wide discovery ofeLife · 2026Article
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- G-Quadruplexes: Structural Diversity and Emerging Roles in Biomolecular Condensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Context-aware sequence-to-function model of human gene regulation.Nature communications · 2026Article
- A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis.BMC genomics · 2026Article
- AI and Its Shifting Roles in the Therapeutic Relationship: Implications for Precision Medicine.Journal of personalized medicine · 2026Review
- Functional annotation of non-coding variants identifies a novel enhancer with activity in neural crest cell-derived lineages.Research square · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 2026Review
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Pervasive Transcription in the Human Genome Exceeds Background Noise.Genome biology and evolution · 2026Article
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- A new super-pangenome pipeline reveals domestication signatures of conserved noncoding sequences in the orange subfamily.Molecular biology and evolution · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches.
Indexed as
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40360798What 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.