ReviewCell reports methods2023
Applications of deep learning in understanding gene regulation.
Review in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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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
39 citing papers in PubMed.
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- Decoding the sequence determinants of locus-specific DNA methylation across human tissues.Nature communications · 2026Article
- IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference.International journal of molecular sciences · 2026Article
- Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis.BMC genomics · 2026Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.bioRxiv : the preprint server for biology · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- EpiExpr: Predicting gene expression using epigenetic data and chromatin interactions.bioRxiv : the preprint server for biology · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Refining sequence-to-activity models by increasing model resolution.Bioinformatics advances · 2026Article
- From reference-genome prediction to causal generalization: sequence-to-function models for plant regulatory genomics.Frontiers in plant science · 2026Review
- Satellite DNA Genomics: The Ongoing Story.International journal of molecular sciences · 2025Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Review
- Environmental adaptations in metagenomes revealed by deep learning.BMC biology · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Refining sequence-to-activity models by increasing model resolution.bioRxiv : the preprint server for biology · 2025Article
- The power of microRNA regulation-insights into immunity and metabolism.FEBS letters · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.
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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.