ArticleNature machine intelligence2022
Evaluating deep learning for predicting epigenomic profiles.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.Briefings in bioinformatics · 2023Pooled it
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
- Distilling the Knowledge of Ensembles for Uncertainty-Aware Genomic Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Article
- From Methylomes to CRISPR Epigenetic Editing: New Paths in Antibiotic Resistance.Pathogens (Basel, Switzerland) · 2025Review
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Iterative improvement of deep learning models using synthetic regulatory genomics.Genome research · 2025Article
- Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Interpretable Transfer Learning for Cancer Drug Resistance: Candidate Target Identification.Current issues in molecular biology · 2025Article
- Base-resolution binding profile prediction of proteins on RNAs with deep learning.Nucleic acids research · 2025Article
- Evaluating the representational power of pre-trained DNA language models for regulatory genomics.Genome biology · 2025Article
- Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds.Genome biology · 2025Article
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.Nucleic acids research · 2025Article
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.
Funding
Abstract
Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recently, quantitative models have emerged to directly predict the experimental coverage values as a regression. As new models continue to emerge with different architectures and training configurations, a major bottleneck is forming due to the lack of ability to fairly assess the novelty of proposed models and their utility for downstream biological discovery. Here we introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. We highlight various modeling choices that affect generalization performance, including a downstream application of predicting variant effects. In addition, we introduce a robustness metric that can be used to enhance model selection and improve variant effect predictions. Our empirical study largely supports that quantitative modeling of epigenomic profiles leads to better generalizability and interpretability.
Identifiers
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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.