AIMC Topic: Chromatin

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Deep learning-based selection of human sperm with high DNA integrity.

Communications biology Jul 3, 2019
Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility ...
Chromatin Semen Analysis DNA Fragmentation Deep Learning Spermatozoa DNA Humans Healthy Volunteers Bayes Theorem Neural Networks, Computer Male Normal Distribution Learning Curve
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DeepHistone: a deep learning approach to predicting histone modifications.

BMC genomics Apr 4, 2019
MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput exp...
Deep Learning Polymorphism, Single Nucleotide Epigenomics Humans Chromosome Mapping Chromatin Chromatin Immunoprecipitation Protein Processing, Post-Translational Gene Expression Regulation Histones
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.) Mar 26, 2019
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...
Transcription Factors Nucleotides Binding Sites Software Deep Learning Protein Binding Chromatin Neural Networks, Computer
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Enhancer prediction with histone modification marks using a hybrid neural network model.

Methods (San Diego, Calif.) Mar 21, 2019
Enhancer is a DNA sequence of a genome that controls transcription of downstream target genes. Enhancers are known to be associated with certain epigenetic signatures. Machine learning tools, such as CSI-ANN, ChromHMM, and RFECS, were developed for p...
Machine Learning Computational Biology Epigenomics Humans Neural Networks, Computer Histone Code Histones Enhancer Elements, Genetic Chromatin
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Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

Methods (San Diego, Calif.) Mar 7, 2019
We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulatio...
Promoter Regions, Genetic Chromatin Computer Simulation Protein Binding Epigenomics Polymers Gene Expression Regulation Genome, Human Humans Machine Learning
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MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.

BMC bioinformatics Feb 28, 2019
BACKGROUND: A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-...
Mice Internet Alleles Genes Chromatin Software Reproducibility of Results Animals Humans Machine Learning
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Dense neural networks for predicting chromatin conformation.

BMC bioinformatics Oct 11, 2018
BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distr...
Neural Networks, Computer Molecular Conformation Chromatin
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Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BMC genomics Aug 13, 2018
BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which f...
Cell Line DNA Copy Number Variations Chromatin Humans Neural Networks, Computer Neoplasms
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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...
Algorithms Gene Expression Regulation Protein Binding RNA, Messenger Humans Chromatin Signal Transduction Cell Line CCCTC-Binding Factor Vitamin D Machine Learning THP-1 Cells Receptors, Calcitriol High-Throughput Nucleotide Sequencing
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Advances in the computational and molecular understanding of the prostate cancer cell nucleus.

Journal of cellular biochemistry Jun 20, 2018
Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components ...
Cell Nucleus Size Prostatic Neoplasms Cell Transformation, Neoplastic Tumor Microenvironment Humans Genomic Instability Image Interpretation, Computer-Assisted Male Machine Learning Cell Nucleus Shape Epigenesis, Genetic Prognosis Chromatin
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