AIMC Topic: Enhancer Elements, Genetic

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Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.

Nature communications Jun 2, 2021
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene ...
Neurodegenerative Diseases High-Throughput Nucleotide Sequencing Base Sequence Mice Computational Biology A549 Cells Enhancer Elements, Genetic Microsatellite Repeats Transcription Initiation Site Transcription Initiation, Genetic Genome, Human Polymorphism, Genetic Promoter Regions, Genetic Humans Neural Networks, Computer Animals Deep Learning
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SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.

BMC research notes Mar 19, 2021
OBJECTIVE: To address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale.
DNA Neural Networks, Computer Deep Learning Enhancer Elements, Genetic Sequence Analysis, DNA
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DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Genomics, proteomics & bioinformatics Feb 11, 2021
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported e...
Humans Neural Networks, Computer Software Sequence Analysis, DNA Enhancer Elements, Genetic Chromatin
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ES-ARCNN: Predicting enhancer strength by using data augmentation and residual convolutional neural network.

Analytical biochemistry Jan 31, 2021
Enhancers are non-coding DNA sequences bound by proteins called transcription factors. They function as distant regulators of gene transcription and participate in the development and maintenance of cell types and tissues. Since experimental validati...
Humans Neural Networks, Computer Computational Biology Enhancer Elements, Genetic Models, Genetic Transcription Factors Databases, Genetic
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Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology Nov 2, 2020
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features...
Binding Sites Enhancer Elements, Genetic Genes, Regulator Mice Computational Biology Models, Genetic Transcription Factors Databases, Genetic Terminology as Topic Animals Deep Learning Gene Regulatory Networks Neural Networks, Computer Computer Simulation
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Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.

Computational and mathematical methods in medicine Oct 18, 2020
Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a h...
Humans Mathematical Concepts Base Composition Databases, Genetic Computational Biology Neural Networks, Computer Base Sequence Models, Genetic Machine Learning Enhancer Elements, Genetic Support Vector Machine Algorithms
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Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

Molecular omics Jun 22, 2020
MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA s...
Enhancer Elements, Genetic Support Vector Machine Databases, Genetic Internet Neural Networks, Computer Male Base Sequence Animals Deep Learning Binding Sites Spermatogenesis Mice Genome
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Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome.

Genome biology Mar 30, 2020
The human epigenome has been experimentally characterized by thousands of measurements for every basepair in the human genome. We propose a deep neural network tensor factorization method, Avocado, that compresses this epigenomic data into a dense, i...
Deep Learning Genomics Gene Expression Epigenome Promoter Regions, Genetic Enhancer Elements, Genetic Humans DNA Replication Timing
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iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

BMC genomics Dec 24, 2019
BACKGROUND: Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, ...
Sequence Analysis, DNA Enhancer Elements, Genetic Neural Networks, Computer
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Ranking of non-coding pathogenic variants and putative essential regions of the human genome.

Nature communications Nov 20, 2019
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. H...
Genes, Reporter Genetic Variation Chromatin Genome, Human Gene Expression Introns Humans Nucleic Acid Conformation Mutation Computer Simulation INDEL Mutation Supervised Machine Learning Enhancer Elements, Genetic DNA
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