AIMC Topic: CCCTC-Binding Factor

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DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Genes Jan 23, 2024
Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facili...
Deep Learning Transcription Factors DNA Precursor T-Cell Lymphoblastic Leukemia-Lymphoma Humans CCCTC-Binding Factor
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InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification.

Genes Mar 30, 2022
Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, ...
Genome Chromatin CCCTC-Binding Factor Neural Networks, Computer Supervised Machine Learning
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Predicting 3D genome folding from DNA sequence with Akita.

Nature methods Oct 12, 2020
In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide foldin...
DNA-Binding Proteins Gene Expression Regulation Genome, Human Neural Networks, Computer Chromosomal Proteins, Non-Histone Humans Cell Cycle Proteins Models, Genetic Cohesins CCCTC-Binding Factor Sequence Analysis, DNA
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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Nature methods Oct 12, 2020
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from...
Genome, Human Chromatin CCCTC-Binding Factor Genomic Structural Variation Genomics Models, Genetic Base Sequence Humans Neural Networks, Computer Computer Simulation
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DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

Genome biology Mar 26, 2020
Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on v...
Mutation Whole Genome Sequencing Cell Cycle Proteins Chromatin CCCTC-Binding Factor Neoplasms Insulator Elements Humans Chromosomal Proteins, Non-Histone Deep Learning Cohesins Cell Line, Tumor
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Machine learning approaches infer vitamin D signaling: Critical impact of vitamin D receptor binding within topologically associated domains.

The Journal of steroid biochemistry and molecular biology Jul 22, 2018
The vitamin D-modulated transcriptome of highly responsive human cells, such as THP-1 monocytes, comprises more than 500 genes, half of which are primary targets. Recently, we proposed a chromatin model of vitamin D signaling demonstrating that nearl...
Chromatin CCCTC-Binding Factor Algorithms Protein Binding RNA, Messenger Humans Signal Transduction Cell Line Machine Learning High-Throughput Nucleotide Sequencing Vitamin D THP-1 Cells Gene Expression Regulation Receptors, Calcitriol
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ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC bioinformatics Nov 14, 2017
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...
Promoter Regions, Genetic CCCTC-Binding Factor Binding Sites Enhancer Elements, Genetic Chromosomes Chromatin Immunoprecipitation Histone Code Sequence Analysis, DNA Cluster Analysis Unsupervised Machine Learning Animals Mice Genomics
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Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.

Nucleic acids research Feb 8, 2025
Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from nascent RNA sequencing data. First, we devise a probabilistic model that predicts nucleotide-specific...
Sequence Analysis, RNA Epigenomics DNA Methylation Humans Neural Networks, Computer Machine Learning Models, Statistical RNA Splice Sites Histones Histone Code Binding Sites CCCTC-Binding Factor Transcription Elongation, Genetic
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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...
Neoplasms Protein Binding Prostatic Neoplasms Chromatin CCCTC-Binding Factor Mutation Female Binding Sites Humans Breast Neoplasms Machine Learning Male
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CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England) Sep 30, 2022
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...
Deep Learning Cell Line Binding Sites Chromatin CCCTC-Binding Factor
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