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).
Current Pharmaceutical Design
Title:Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases
Volume: 24 Issue: 34
Author(s): Shijie Fan, Yu Chen, Cheng Luo and Fanwang Meng*
Affiliation:
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON,Canada
Keywords: Machine learning, precision medicine, epigenetics, deep learning, genome, Human Genome Project (HGP).
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).
Export Options
About this article
Cite this article as:
Fan Shijie , Chen Yu , Luo Cheng and Meng Fanwang *, Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases, Current Pharmaceutical Design 2018; 24 (34) . https://dx.doi.org/10.2174/1381612824666181112114228
DOI https://dx.doi.org/10.2174/1381612824666181112114228 |
Print ISSN 1381-6128 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4286 |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
C-KIT Signaling in Cancer Treatment
Current Pharmaceutical Design Use of Compound Chinese Medicine in the Treatment of Lung Cancer
Current Drug Discovery Technologies New Therapies for Patients with Chronic Lymphocytic Leukemia
Current Cancer Therapy Reviews Rational Design of CPP-based Drug Delivery Systems: Considerations from Pharmacokinetics
Current Pharmaceutical Biotechnology Microfluidic Assembly of Lipid Nanoparticles for Delivery of Antisense Oligonucleotides
Current Pharmaceutical Biotechnology Recent Advances in Peptide-Based Approaches for Cancer Treatment
Current Medicinal Chemistry A Disposable, Highly Sensitive Biosensing System: Determination of Haptoglobin as a Significant Acute Phase Biomarker
Current Analytical Chemistry Innovations in siRNA Research: A Technology Comes of Age
Recent Patents on Anti-Infective Drug Discovery Alkannins and Shikonins: A New Class of Wound Healing Agents
Current Medicinal Chemistry Obesity and Cancer: Biological Links and Treatment Implications
Current Cancer Drug Targets Molecular Markers of Angiogenesis and Metastasis in Lines of Oral Carcinoma after Treatment with Melatonin
Anti-Cancer Agents in Medicinal Chemistry 6,7-Dimethoxyquinazolines as Potential Cytotoxic Agents: Synthesis and in vitro Activity
Letters in Drug Design & Discovery Cytochrome P450-Activated Prodrugs: Targeted Drug Delivery
Current Medicinal Chemistry The Molecular Basis of Notch Signaling Regulation: A Complex Simplicity
Current Molecular Medicine New Spirocyclic Hydroxamic Acids as Effective Antiproliferative Agents
Anti-Cancer Agents in Medicinal Chemistry Effect of Oxidative Stress on the Pharmacokinetics of Clomipramine in Rats Treated with Ferric-Nitrilotriacetate
Drug Metabolism Letters A Review on the Synthesis and Anti-cancer Activity of 2-substituted Quinolines
Anti-Cancer Agents in Medicinal Chemistry MicroRNAs in Platelet Biogenesis and Function: Implications in Vascular Homeostasis and Inflammation
Current Vascular Pharmacology Proinflammatory Cytokines in Breast Cancer: Mechanisms of Action and Potential Targets for Therapeutics
Current Drug Targets CT Image Reconstruction Using NLMfuzzyCD Regularization Method
Current Medical Imaging