… overview of epigenetics and the potential of ML in clinical applications. The interested reader
may refer to the cited literature on the different topics of epigenetics and machine learning. …

… of molecular epigenetic data in advanced machine learning … selection, and machine learning
on epigenetic data sets is … Active Learning (ACL) to address the expense of generating …

… Epigenetic classifiers can be used in combination with current tools to … epigenetic biomarkers
are currently used clinically. Applications of machine learning in clinical epigenetics hold …

… The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A
novel analysis pipeline was employed to assess DNA methylation patterns between peripheral …

… Although AI is a broad concept, machine learning (ML) is the application of AI into a
system or machine, which helps it to self-learn and improve continually. Machine learning …

… -computational machine learning approach, conducted using DNA Methylation (DNAm) data,
to accurately diagnose food allergies and potentially find epigenetic targets for the disease. …

… Therefore, applying machine-learning methods to epigenetic and clinicopathological data
… for routine clinical practice. In this study, we constructed a machine learning classifier based …

… the advantages of machine learning and deep learning technologies for analysis of medical
… , but a number of machine learning and deep learning technologies possess all four features…

… Machine learning applications have played crucial roles in addressing multiple questions
related to the basic biology of epigenetic elements, their role in gene regulation, and the utility …

… outperformed models with clinical characteristics or DNA methylation levels alone, published
chronic heart failure risk prediction models and other benchmark machine learning models. …