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Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

[ Vol. 24 , Issue. 34 ]

Author(s):

Shijie Fan, Yu Chen, Cheng Luo and Fanwang Meng*   Pages 3998 - 4006 ( 9 )

Abstract:


Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine.

Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed.

Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases.

Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

Keywords:

Machine learning, precision medicine, epigenetics, deep learning, genome, Human Genome Project (HGP).

Affiliation:

Drug Discovery & Design Center, State Key Lab Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, Drug Discovery & Design Center, State Key Lab Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, Drug Discovery & Design Center, State Key Lab Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON



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