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DNA Methylation

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moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

PeerJ
Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation o...

DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

Computers in biology and medicine
N-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Tradit...

A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing.

Nature communications
Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequenc...

Biologically informed deep learning for explainable epigenetic clocks.

Scientific reports
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, ...

Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Brain pathology (Zurich, Switzerland)
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the exist...

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.

BMC genomics
BACKGROUND: DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for und...

SNN6mA: Improved DNA N6-methyladenine site prediction using Siamese network-based feature embedding.

Computers in biology and medicine
DNA N6-methyladenine (6mA) is one of the most common and abundant modifications, which plays essential roles in various biological processes and cellular functions. Therefore, the accurate identification of DNA 6mA sites is of great importance for a ...

DeepMethylation: a deep learning based framework with GloVe and Transformer encoder for DNA methylation prediction.

PeerJ
DNA methylation is a crucial topic in bioinformatics research. Traditional wet experiments are usually time-consuming and expensive. In contrast, machine learning offers an efficient and novel approach. In this study, we propose DeepMethylation, a no...

Precision epigenetics provides a scalable pathway for improving coronary heart disease care globally.

Epigenomics
Coronary heart disease (CHD) is the world's leading cause of death. Up to 90% of all CHD deaths are preventable, but effective prevention of this mortality requires more scalable, precise methods for assessing CHD status and monitoring treatment resp...

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

PLoS computational biology
DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gai...