AIMC Topic: DNA Methylation
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Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Bioinformatics (Oxford, England)
Sep 2, 2024
MOTIVATION: 5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting ...
BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.
Briefings in bioinformatics
Jul 25, 2024
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratificati...
Deep centroid: a general deep cascade classifier for biomedical omics data classification.
Bioinformatics (Oxford, England)
Feb 1, 2024
MOTIVATION: Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent bia...
Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.
Bioinformatics (Oxford, England)
Nov 1, 2023
SUMMARY: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of dat...
Application of deep learning in cancer epigenetics through DNA methylation analysis.
Briefings in bioinformatics
Sep 22, 2023
DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, ...
A review of methods for predicting DNA N6-methyladenine sites.
Briefings in bioinformatics
Jan 19, 2023
Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA si...
A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2023
Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline...
Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.
GigaScience
Oct 19, 2022
BACKGROUND: DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing.
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.
Briefings in bioinformatics
Mar 10, 2022
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are cost...
Detection of transcription factors binding to methylated DNA by deep recurrent neural network.
Briefings in bioinformatics
Jan 17, 2022
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confir...
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