ArticleNucleic acids research2023
A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed.
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping.Bioinformatics (Oxford, England) · 2026Article
- Context-aware sequence-to-function model of human gene regulation.Nature communications · 2026Article
- A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.Briefings in bioinformatics · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
- An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation.Nature communications · 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
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- A comprehensive evaluation of self-attention for detecting regulatory feature interactions.NAR genomics and bioinformatics · 2026Article
- mwHIT: accelerated and accurate histone modification imputation using multi-scale window attention.Frontiers in genetics · 2026Article
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.Nucleic acids research · 2025Article
- A Chromatin-Structure-Guided Framework for Predictive and Interpretable Regulatory Genomics.bioRxiv : the preprint server for biology · 2025Article
- Predicting the regulatory genome.Nature reviews. Genetics · 2025Article
- Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge.bioRxiv : the preprint server for biology · 2025Article
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
- ChromoGen: Diffusion model predicts single-cell chromatin conformations.Science advances · 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
4 authors.
Funding
Abstract
Many deep learning approaches have been proposed to predict epigenetic profiles, chromatin organization, and transcription activity. While these approaches achieve satisfactory performance in predicting one modality from another, the learned representations are not generalizable across predictive tasks or across cell types. In this paper, we propose a deep learning approach named EPCOT which employs a pre-training and fine-tuning framework, and is able to accurately and comprehensively predict multiple modalities including epigenome, chromatin organization, transcriptome, and enhancer activity for new cell types, by only requiring cell-type specific chromatin accessibility profiles. Many of these predicted modalities, such as Micro-C and ChIA-PET, are quite expensive to get in practice, and the in silico prediction from EPCOT should be quite helpful. Furthermore, this pre-training and fine-tuning framework allows EPCOT to identify generic representations generalizable across different predictive tasks. Interpreting EPCOT models also provides biological insights including mapping between different genomic modalities, identifying TF sequence binding patterns, and analyzing cell-type specific TF impacts on enhancer activity.
Indexed as
Identifiers
What Socratic 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 Socratic graph.