AIMC Topic: Chromatin
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deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.
Briefings in bioinformatics
Mar 4, 2025
MOTIVATION: Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current method...
Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.
Nucleic acids research
Feb 8, 2025
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors li...
Machine and Deep Learning Methods for Predicting 3D Genome Organization.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
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 expressi...
DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.
Briefings in bioinformatics
Nov 22, 2024
The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq...
A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.
Briefings in bioinformatics
Nov 22, 2024
Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologi...
A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.
Briefings in functional genomics
Sep 27, 2024
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genom...
Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.
Nucleic acids research
Aug 12, 2024
CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are...
The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model.
Nucleic acids research
Jul 22, 2024
Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven distribution of TF binding sites across the genome poses challenges in understanding how this distr...
scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.
Bioinformatics (Oxford, England)
Jun 28, 2024
SUMMARY: Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the hi...
Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extreme...
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