AIMC Topic: Chromatin

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HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

Briefings in bioinformatics Mar 27, 2024
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell label...
Reproducibility of Results Chromatin Neural Networks, Computer
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ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion.

Bioinformatics (Oxford, England) Nov 1, 2023
MOTIVATION: The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithfu...
Chromatin Replication Origin DNA Replication Artificial Intelligence Base Sequence
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Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England) Jun 1, 2023
MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...
Deep Learning Algorithms Single-Cell Analysis Cluster Analysis Chromatin
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DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.

Briefings in bioinformatics Mar 19, 2023
Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. Ho...
Genomics Base Sequence Neural Networks, Computer Chromatin Genome
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AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Nucleic acids research Jan 6, 2023
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...
Single-Cell Analysis Knowledge Bases Child, Preschool Child Chromatin Aging Middle Aged Infant Adolescent Young Adult Adult Aged, 80 and over Humans Infant, Newborn Aged
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DeepZ: A Deep Learning Approach for Z-DNA Prediction.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, c...
Chromatin Histone Code DNA, Z-Form Neural Networks, Computer Deep Learning
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Deep Learning on Chromatin Accessibility.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
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...
Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing Chromatin Deep Learning Genomics
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DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Bioinformatics (Oxford, England) Jan 1, 2023
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...
Chromatin Chromosomes Deep Learning Regulatory Sequences, Nucleic Acid Promoter Regions, Genetic
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Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research Oct 28, 2022
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...
Transcription Factors Chromatin Humans Neoplasms Precision Medicine Deep Learning
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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) Sep 30, 2022
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...
Chromatin Cell Line Deep Learning CCCTC-Binding Factor Binding Sites
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