ArticleGenome research2020
Cross-species analysis of enhancer logic using deep learning.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- UnlockingNAR genomics and bioinformatics · 2026Article
- Gene regulatory innovations from transposable elements in primate cerebellum development.Nature communications · 2026Article
- Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.Science advances · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- Positional interpretation of cis-regulatory code and nucleosome organization with deep learning models.Nature communications · 2026Article
- Decoding common and rare noncoding variant effects across cellular and developmental contexts.Nature genetics · 2026Article
- Challenges in predicting chromatin accessibility differences between species.NAR genomics and bioinformatics · 2026Article
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.bioRxiv : the preprint server for biology · 2026Article
- CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.Nature methods · 2026Article
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Multi-omic profiling of human and mouse dorsal root ganglia enables targeted gene delivery to nociceptors.bioRxiv : the preprint server for biology · 2026Article
- In silico genome transplants and the cis-regulatory basis of biodiversity.Trends in genetics : TIG · 2026Review
- The evolution of gene regulation in mammalian cerebellum development.Science (New York, N.Y.) · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Motif-based models accurately predict cell type-specific distal regulatory elements.Nature communications · 2025Article
21 more citing papers are in PubMed but not listed here.
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19 authors.
Funding
Abstract
Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type-specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of
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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.