AIMC Topic: Chromatin

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Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Apr 28, 2023
Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin...
Humans Deep Learning RNA Chromatin Multiomics Membrane Proteins
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A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences Mar 1, 2023
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular sta...
Chromatin Chromatin Immunoprecipitation Sequencing Deep Learning Transcription Factors Single-Cell Analysis Gene Expression Regulation
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DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data.

BMC bioinformatics Dec 12, 2022
BACKGROUND: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional an...
Neural Networks, Computer Software Chromatin Promoter Regions, Genetic Transcription Initiation Site
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iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

BMC bioinformatics Nov 14, 2022
Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement struc...
Deep Learning DNA Neural Networks, Computer Enhancer Elements, Genetic Chromatin
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Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology Oct 14, 2022
Single-cell multi-omics technologies enable comprehensive interrogation of cellular regulation, yet most single-cell assays measure only one type of activity-such as transcription, chromatin accessibility, DNA methylation, or 3D chromatin architectur...
Chromatin Supervised Machine Learning Databases, Factual
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DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

PLoS computational biology Oct 7, 2022
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta...
Transcription Factors Genome Chromosomes Chromatin Deep Learning
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Characterizing collaborative transcription regulation with a graph-based deep learning approach.

PLoS computational biology Jun 6, 2022
Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulat...
Gene Expression Regulation Binding Sites Chromatin Transcription Factors DNA Humans Protein Binding Deep Learning
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Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

Journal of molecular biology Jun 2, 2022
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. Howeve...
Machine Learning Chromosomes Chromosome Mapping Cell Nucleus Chromatin Imaging, Three-Dimensional Genome Sequence Analysis, DNA
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Chromatin interaction-aware gene regulatory modeling with graph attention networks.

Genome research Apr 8, 2022
Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach...
Neural Networks, Computer Gene Regulatory Networks Transcription Factors Gene Expression Regulation Regulatory Sequences, Nucleic Acid Chromatin Binding Sites
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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, ...
Chromatin CCCTC-Binding Factor Supervised Machine Learning Genome Neural Networks, Computer
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