AIMC Topic: Histone Code
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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...
RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.
BMC genomics
Jul 20, 2018
BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectabi...
ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.
BMC bioinformatics
Nov 14, 2017
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...
McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.
Genome biology
Oct 26, 2017
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...
Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.
Nucleic acids research
Feb 8, 2025
Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from nascent RNA sequencing data. First, we devise a probabilistic model that predicts nucleotide-specific...
Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.
Nucleic acids research
Feb 8, 2025
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...
Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.
Nucleic acids research
Jul 22, 2024
The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction i...
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...
TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets.
Bioinformatics (Oxford, England)
Dec 7, 2021
MOTIVATION: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information....
Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.
Nucleic acids research
Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Linkage Disequilibrium
Genetic Diseases, Inborn
Chromatin Immunoprecipitation
Histone Code
Humans
Organ Specificity
Deep Learning
Molecular Sequence Annotation
Causality
Genome-Wide Association Study
Binding Sites
Epigenome
Models, Genetic
Datasets as Topic
Polymorphism, Single Nucleotide
Transcription Factors
Epigenomics
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