ArticleiScience2024
Predicting A/B compartments from histone modifications using deep learning.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed, 8 citations in OpenAlex.
- An Energy Model Based on Molecular Structure for Predicting Histone Modification Levels at lncRNA Promoter Regions in HepG2 Cells.International journal of molecular sciences · 2026Article
- THC-net: an attention-based deep learning model for chromatin compartment prediction from histone modifications.BMC bioinformatics · 2026Article
- Multi-dimensional epigenomic dynamics converge on H3K4-mediated regulation of low-COPlant communications · 2025Article
- Epigenetics is all you need: A transformer to decode chromatin structural compartments from the epigenome.PLoS computational biology · 2025Article
- MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.PLoS computational biology · 2025Article
- COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.Genomics, proteomics & bioinformatics · 2025Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- Chromosome compartmentalization: causes, changes, consequences, and conundrums.Trends in cell biology · 2024Review
- Interplay between epigenome and 3D chromatin structure.BMB reports · 2023Review
- Assignment of the somatic A/B compartments to chromatin domains in giant transcriptionally active lampbrush chromosomes.Epigenetics & chromatin · 2023Article
- Considerations and caveats for analyzing chromatin compartments.Frontiers in molecular biosciences · 2023Review
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
The three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization. To address this, we present a prediction tool called compartment prediction using recurrent neural networks (CoRNN), which predicts compartmentalization of 3D genome using histone modification enrichment. CoRNN demonstrates robust cross-cell-type prediction of A/B compartments with an average AuROC of 90.9%. Cell-type-specific predictions align well with known functional elements, with H3K27ac and H3K36me3 identified as highly predictive histone marks. We further investigate our mispredictions and found that they are located in regions with ambiguous compartmental status. Furthermore, our model's generalizability is validated by predicting compartments in independent tissue samples, which underscores its broad applicability.
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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 Socratic graph.