AIMC Topic: DNA Methylation
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Effective training of nanopore callers for epigenetic marks with limited labelled data.
Open biology
Jun 12, 2024
Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been tra...
Using a hybrid neural network architecture for DNA sequence representation: A study on N-methylcytosine sites.
Computers in biology and medicine
May 27, 2024
N-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous inv...
Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.
Nature medicine
May 17, 2024
Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven mea...
Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients.
Clinical epigenetics
May 15, 2024
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
Apr 22, 2024
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
Mar 28, 2024
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
Feb 26, 2024
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
Feb 16, 2024
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
Feb 16, 2024
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
Jan 15, 2024
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, ...
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