ArticleEpigenetics & chromatin2020
Integrative prediction of gene expression with chromatin accessibility and conformation data.
Article in Epigenetics & chromatin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 56 citations in OpenAlex.
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.Genome biology · 2026Article
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- A machine learning approach for multimodal data fusion for survival prediction in cancer patients.NPJ precision oncology · 2025Article
- Macrophage memory emerges from coordinated transcription factor and chromatin dynamics.Cell systems · 2025Article
- Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Enhancer-Gene Interactions Using Chromatin-Conformation Capture and Epigenome Data Using STARE.Methods in molecular biology (Clifton, N.J.) · 2025Article
- dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.Briefings in bioinformatics · 2024Article
- Gene therapy for liver diseases - progress and challenges.Nature reviews. Gastroenterology & hepatology · 2023Review
- A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters.STAR protocols · 2023Article
- The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data.Bioinformatics (Oxford, England) · 2023Article
- 3D genome organization links non-coding disease-associated variants to genes.Frontiers in cell and developmental biology · 2022Review
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
- Integrative analysis of epigenetics data identifies gene-specific regulatory elements.Nucleic acids research · 2021Article
- Differential contribution to gene expression prediction of histone modifications at enhancers or promoters.PLoS computational biology · 2021Article
- Article
- Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources.BMC bioinformatics · 2021Article
- A hierarchical regulatory network analysis of the vitamin D induced transcriptome reveals novel regulators and complete VDR dependency in monocytes.Scientific reports · 2021Article
- Article
- Transcription factors: building hubs in the 3D space.Cell cycle (Georgetown, Tex.) · 2020Review
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundEnhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.
resultsWe have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.
conclusionWe present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.
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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.