ReviewMethods in molecular biology (Clifton, N.J.)2025
Machine and Deep Learning Methods for Predicting 3D Genome Organization.
Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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
8 citing papers in PubMed.
- The cardiac 4D nucleome: nuclear and chromatin dynamics across development, disease and ageing.Nature reviews. Cardiology · 2026Review
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- CT-TADB predicts TAD boundaries without Hi-C by integrating DNA sequences and epigenomic features.NPJ systems biology and applications · 2026Article
- In silico genome transplants and the cis-regulatory basis of biodiversity.Trends in genetics : TIG · 2026Review
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- Review
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
- GenomicLinks: deep learning predictions of 3D chromatin interactions in the maize genome.NAR genomics and bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.
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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.