Applications of Machine Learning Methods in Health Outcomes Research: Heart Failure in Women

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).

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Applications of machine learning methods for engineering risk assessment – A review

Safety Science ◽

10.1016/j.ssci.2019.09.015 ◽

2020 ◽

Vol 122 ◽

pp. 104492 ◽

Cited By ~ 19

Author(s):

Jeevith Hegde ◽

Børge Rokseth

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Learning Methods ◽

Machine Learning Methods ◽

Applications Of Machine Learning

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Recent Advances on the Design Automation for Performance-Optimized Fiber Reinforced Polymer Composite Components

Journal of Composites Science ◽

10.3390/jcs4020061 ◽

2020 ◽

Vol 4 (2) ◽

pp. 61

Author(s):

Yi Di Boon ◽

Sunil Chandrakant Joshi ◽

Somen Kumar Bhudolia ◽

Goram Gohel

Keyword(s):

Machine Learning ◽

Polymer Composite ◽

Design Automation ◽

Material Design ◽

Fiber Reinforced Polymer ◽

Fiber Reinforced ◽

Learning Methods ◽

Machine Learning Methods ◽

Reinforced Polymer ◽

Applications Of Machine Learning

Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.

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