ArticleScientific reports2024
Predicting CTCF cell type active binding sites in human genome.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Epigenetic Alterations in Meningiomas-A Review.Biomedicines · 2026Review
- 3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.Functional & integrative genomics · 2026Article
- The Biological Function of Genome Organization.International journal of molecular sciences · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
The CCCTC-binding factor (CTCF) is pivotal in orchestrating diverse biological functions across the human genome, yet the mechanisms driving its cell type-active DNA binding affinity remain underexplored. Here, we collected ChIP-seq data from 67 cell lines in ENCODE, constructed a unique dataset of cell type-active CTCF binding sites (CBS), and trained convolutional neural networks (CNN) to dissect the patterns of CTCF binding activity. Our analysis reveals that transcription factors RAD21/SMC3 and chromatin accessibility are more predictive compared to sequence motifs and histone modifications. Integrating them together achieved AUPRC values consistently above 0.868, highlighting their utility in deciphering CTCF transcription factor binding dynamics. This study provides a deeper understanding of the regulatory functions of CTCF via machine learning framework.
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Registered trials
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