AIMC Topic: DNA Methylation
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A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.
Journal of digital imaging
Jan 5, 2023
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (...
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.
Computers in biology and medicine
Jan 4, 2023
BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT met...
Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.
Genes
Aug 29, 2022
DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to t...
Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.
Proceedings of the National Academy of Sciences of the United States of America
Aug 15, 2022
There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challengin...
A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation.
BMC bioinformatics
Jul 11, 2022
BACKGROUND: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible ...
Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species.
Methods (San Diego, Calif.)
Apr 25, 2022
DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and...
GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.
Methods (San Diego, Calif.)
Feb 9, 2022
MOTIVATION: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. ...
An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2022
It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites...
DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.
Computational intelligence and neuroscience
Jan 24, 2022
PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine lea...
A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features.
Clinical epigenetics
Jan 19, 2022
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved som...
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