AIMC Topic: Chromatin

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DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

Briefings in bioinformatics Jul 20, 2021
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some comput...
Chromatin YY1 Transcription Factor Databases, Factual Deep Learning HCT116 Cells K562 Cells Models, Biological Humans
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Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics Jul 20, 2021
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...
Humans Databases, Factual Transcriptome Deep Learning Models, Biological K562 Cells Single-Cell Analysis Chromatin
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Interpretation of deep learning in genomics and epigenomics.

Briefings in bioinformatics May 20, 2021
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies tow...
Neural Networks, Computer Genomics Computational Biology Epigenesis, Genetic Deep Learning Protein Binding RNA Chromatin DNA Gene Expression
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Identification of Gene Regulatory Networks from Single-Cell Expression Data.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions...
Chromatin Chromatin Immunoprecipitation Sequencing Sequence Analysis, DNA Programming Languages Software Single-Cell Analysis Gene Regulatory Networks Computational Biology RNA-Seq Machine Learning
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Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data.

Bioinformatics (Oxford, England) Dec 30, 2020
MOTIVATION: Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial struc...
Chromatin Epigenomics Genome Neural Networks, Computer Epigenesis, Genetic
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Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Segmentation of cell nuclei in fluorescence microscopy images provides valuable information about the shape and size of the nuclei, its chromatin texture and DNA content. It has many applications such as cell tracking, counting and classification. In...
Microscopy, Fluorescence Cell Nucleus Chromatin Algorithms Deep Learning
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HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.

Bioinformatics (Oxford, England) Nov 1, 2019
MOTIVATION: High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammal...
Humans Neural Networks, Computer Animals Mice Software Chromatin Genome Chromosomes
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GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.

Bioinformatics (Oxford, England) Jul 15, 2019
SUMMARY: Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpreta...
Support Vector Machine Protein Binding Chromatin
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DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Nucleic acids research Jun 4, 2019
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. Ho...
Chromatin Cells, Cultured Deep Learning Gene Expression Regulation Genome-Wide Association Study Promoter Regions, Genetic Computational Biology Humans Regulatory Sequences, Nucleic Acid High-Throughput Nucleotide Sequencing Algorithms
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Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription...
Sequence Analysis, DNA Genomics Neural Networks, Computer DNA Transcription Factors Protein Binding Binding Sites High-Throughput Nucleotide Sequencing Chromatin
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