AIMC Topic: Chromatin

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DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator.

Bioinformatics (Oxford, England) Jul 11, 2022
MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neit...
Deep Learning Genome-Wide Association Study Genomics Chromatin Humans COVID-19
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seqgra: principled selection of neural network architectures for genomics prediction tasks.

Bioinformatics (Oxford, England) Apr 28, 2022
MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their con...
Software Genomics Regulatory Sequences, Nucleic Acid Neural Networks, Computer Chromatin
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DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research Apr 8, 2022
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...
Nucleosomes Cyclization Deep Learning Mice DNA Binding Sites Mammals Software Chromatin Saccharomyces cerevisiae Animals
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scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.

Nucleic acids research Jan 7, 2022
Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populati...
Databases, Genetic Unsupervised Machine Learning Internet Single-Cell Analysis Gene Regulatory Networks Mice Organ Specificity Drosophila melanogaster Molecular Sequence Annotation Cell Lineage Genetic Heterogeneity Gene Expression Regulation Promoter Regions, Genetic Software Animals Humans Eukaryotic Cells Enhancer Elements, Genetic Chromatin Consensus Sequence
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Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...
Neural Networks, Computer Machine Learning Microscopy, Fluorescence Chromatin
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TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets.

Bioinformatics (Oxford, England) Dec 7, 2021
MOTIVATION: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information....
Chromatin Histone Code Chromatin Immunoprecipitation Sequencing Deep Learning Regulatory Sequences, Nucleic Acid
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Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

Nucleic acids research Nov 8, 2021
The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility ...
Eukaryotic Cells Humans Neural Networks, Computer Genome, Human Cell Lineage Epigenesis, Genetic Machine Learning Chromatin Histones Atlases as Topic Transcription Factors Chromatin Immunoprecipitation Software Binding Sites Cell Communication Protein Binding
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iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics Sep 2, 2021
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...
Arabidopsis Chromatin Deoxyribonuclease I Internet Protein Binding Transcription, Genetic Datasets as Topic Oryza Humans Transcription Factors Enhancer Elements, Genetic Promoter Regions, Genetic Genetic Loci Software DNA Deep Learning
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A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics Sep 2, 2021
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...
Chromatin Humans Neural Networks, Computer CCCTC-Binding Factor Software MCF-7 Cells Genome, Human K562 Cells Datasets as Topic Molecular Conformation Nucleotide Motifs Gene Expression Regulation
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Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

Briefings in bioinformatics Sep 2, 2021
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as h...
Supervised Machine Learning Base Sequence Genome, Human Chromatin Chromatin Assembly and Disassembly Epigenesis, Genetic Sequence Analysis, DNA Unsupervised Machine Learning Humans
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