Machine learning methods in precision medicine targeting epigenetics diseases

Machine learning is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. It is clearly visible that, machine learning is essential in this era in which we are living in, when there is huge amount of epigenetic data present coming from experiments and the clinic. Machine learning can aid in detection of epigenetic features in a given genome. Machine learning also helps in finding similarities and relationships between phenotypes and modifications in histones and genes. It also helps to accelerate the screening of lead compounds which are targeting markers for epigenetics diseases. Along with these uses, there are many other aspects around the study on epigenetics, which consequently bring us closer to realize our current hopes in precision medicine. Many new studies in precision medicine targeting epigenetic disease biomarkers are therefore now possible because of the fact that machine learning algorithms have accelerated processes used for data analyses. Therefore, in order to take full benefit of machine learning algorithms, one should get familiar with the pros and cons of them as it is one way to bring optimum use out of them.

In this review, the authors discuss the fundamentals and the important points of machine learning, the applications of machine learning, the methods which are used in the field of epigenetics and their features. The advantages and disadvantages of using machine language for research in epigenetics are also discussed.

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