ArticleNature communications2022
Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 37 citations in OpenAlex.
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.Genome biology · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- Lactylation omics of rabbit rotator cuff tear reveals differentially modified proteins and metabolic relating therapy targets.Frontiers in medicine · 2026Article
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
- Towards universal modeling of transcript isoform expression levels.bioRxiv : the preprint server for biology · 2025Article
- Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.ArXiv · 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
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.Briefings in bioinformatics · 2024Article
- DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.Plant communications · 2024Article
- CatLearning: highly accurate gene expression prediction from histone mark.Briefings in bioinformatics · 2024Article
- Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.Briefings in bioinformatics · 2024Article
- CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms.Genome biology · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
3 authors at 1 institution in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
The quantitative characterization of the transcriptional control by histone modifications has been challenged by many computational studies, but most of them only focus on narrow and linear genomic regions around promoters, leaving a room for improvement. We present Chromoformer, a transformer-based, three-dimensional chromatin conformation-aware deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes in gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of transcriptional regulation involving from core promoters to distal elements in contact with promoters through three-dimensional chromatin interactions. In-depth interpretation of Chromoformer reveals that it adaptively utilizes the long-range dependencies between histone modifications associated with transcription initiation and elongation. We also show that the quantitative kinetics of transcription factories and Polycomb group bodies can be captured by Chromoformer. Together, our study highlights the great advantage of attention-based deep modeling of complex interactions in epigenomes.
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