AIMC Topic: DNA Methylation

Clear Filters Showing 111 to 120 of 285 articles

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients.

Clinical epigenetics
BACKGROUND: There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles.

Computers in biology and medicine
Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognize...

Inference of Developmental Hierarchy and Functional States of Exhausted T Cells from Epigenetic Profiles with Deep Learning.

Journal of chemical information and modeling
Exhausted T cells are a key component of immune cells that play a crucial role in the immune response against cancer and influence the efficacy of immunotherapy. Accurate assessment and measurement of T-cell exhaustion (TEX) are critical for understa...

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 ...