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Chromatin

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Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BMC genomics
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
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
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
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
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...

A knowledgebase of the human Alu repetitive elements.

Journal of biomedical informatics
Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participate in multiple processes related with genome regulation and compartmentalization. Moreover, they hav...

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BMC bioinformatics
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...

deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.

Briefings in bioinformatics
MOTIVATION: Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current method...

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

Nucleic acids research
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors li...