ArticleBriefings in bioinformatics2025
A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.Briefings in bioinformatics · 2026Article
- TSProm: deep learning framework to predict tissue-specific regulatory logic.NAR genomics and bioinformatics · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- MOGANet: A Multi-omics Graph Attention Network for Cancer Diagnosis and Biomarker Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
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- CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.Briefings in bioinformatics · 2025Article
- ATOMIC: a graph attention network for atopic dermatitis prediction using human gut microbiome.Frontiers in immunology · 2025Article
- Multi-Omics Analysis and Experimental Validation Identify RAD51 as a Key Biomarker in OSCC.IET systems biologyArticle
- Identification of Chemokine-Related Genes Derived From T and NK Cells in the Tumour Microenvironment of Ovarian Cancer Based on scRNA-Seq.IET systems biologyArticle
- Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation.IET systems biologyArticle
- Identification of an M1 Macrophages-Related Signature for Predicting the Survival and Therapeutic Response in Gastric Cancer.IET systems biologyArticle
- Metabolic Reprogramming in Recurrent Spontaneous Abortion: Key Biomarkers Identification and Diagnostic Model Development.IET systems biologyArticle
- Machine Learning-Based Integrative Analysis Identifies SUMOylation-Related Genes Underlying the Immune Heterogeneity of Sepsis.IET systems biologyArticle
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- Pan-Cancer Analysis of CLDN3 and Its Contribution to 5-FU Resistance in Colorectal Cancer.IET systems biologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A comprehensive understanding of chromatin interaction networks is crucial for unraveling the regulatory mechanisms of gene expression. While various computational methods have been developed to predict chromatin interactions and address the limitations and high costs of high-throughput experimental techniques, their performance is often overestimated due to the specificity of chromatin interaction data. In this study, we proposed Inter-Chrom, a novel deep learning model integrating dynamic tokenization, DNABERT's word embedding, and the efficient channel attention mechanism to identify chromatin interactions using sequence and genomic features, leveraging a newly curated dataset. Experimental results demonstrate that Inter-Chrom outperforms existing methods on three cell line datasets. Additionally, we proposed a novel method for calculating motif importance and analyzed the motifs with high importance scores identified through this method, including those that have been extensively studied and others that have received limited attention to date. Inter-Chrom's robustness for input variations and superior ability to leverage sequence features position it as a powerful tool for advancing chromatin interaction research. The source code of Inter-Chrom is freely available at https://github.com/HaoWuLab-Bioinformatics/Inter-Chrom.
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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.