ArticleGenome research2022
Chromatin interaction-aware gene regulatory modeling with graph attention networks.
Article in Genome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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.
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Who cites it
68 citing papers in PubMed.
- OrchAlign: orchestrated multimodal alignment for gene expression prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 2026Article
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Positive selection on brain cis-regulatory elements in the human lineage drives changes in gene expression and susceptibility to neuropsychiatric disorders.HGG advances · 2026Article
- A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.Briefings in bioinformatics · 2026Article
- Massively parallel reporter assay-informed modeling improves prediction of context-specific enhancer-gene regulatory interactions.Nucleic acids research · 2026Article
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Refining sequence-to-expression modelling with chromatin accessibility.Bioinformatics (Oxford, England) · 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
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- A comprehensive evaluation of self-attention for detecting regulatory feature interactions.NAR genomics and bioinformatics · 2026Article
- EpGAT: integrating epigenetics and 3D genome structure to predict alternative splicing and polyadenylation.Briefings in bioinformatics · 2026Article
- Modeling nascent transcription from chromatin landscape and structure with CLASTER.Genome biology · 2026Article
- A scalable computational framework for predicting gene expression from candidateGenome research · 2026Article
8 more citing papers are in PubMed but not listed here.
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
Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements up to 2 Mb away in the genome, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than the state-of-the-art deep learning methods for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both convolutional neural networks (CNNs) and the recently proposed activity-by-contact model. Sequence-based GraphReg also accurately predicts direct transcription factor (TF) targets as validated by CRISPRi TF knockout experiments via in silico ablation of TF binding motifs. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.
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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.