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Chromatin

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Characterizing collaborative transcription regulation with a graph-based deep learning approach.

PLoS computational biology
Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulat...

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England)
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...

Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology
Single-cell multi-omics technologies enable comprehensive interrogation of cellular regulation, yet most single-cell assays measure only one type of activity-such as transcription, chromatin accessibility, DNA methylation, or 3D chromatin architectur...

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Inter...

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

PLoS computational biology
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta...

iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

BMC bioinformatics
Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement struc...

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data.

BMC bioinformatics
BACKGROUND: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional an...

Deep Learning on Chromatin Accessibility.

Methods in molecular biology (Clifton, N.J.)
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been show...

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Bioinformatics (Oxford, England)
MOTIVATION: Promoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing techno...

AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Nucleic acids research
Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell le...