AIMC Topic: Chromatin
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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...
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 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...
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-...
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...
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...
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...
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 ...
De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture.
Proceedings of the National Academy of Sciences of the United States of America
Oct 31, 2017
Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing ...
EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.
Scientific reports
Dec 8, 2016
We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem c...
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