Machine learning in cardiovascular genomics, proteomics, and drug discovery

Ming Wai Yeung; Jan Walter Benjamins; Pim Van Der Harst; Luis Eduardo Juarez-Orozco

doi:10.1016/B978-0-12-820273-9.00014-2

Machine learning in cardiovascular genomics, proteomics, and drug discovery

Ming Wai Yeung, Jan Walter Benjamins, Pim Van Der Harst, Luis Eduardo Juarez-Orozco

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

This chapter discusses the current status and challenges in applying machine learning in three closely connected fields, namely genomics, proteomics, and drug discovery. Usage of machine learning methods are described and compared in the context of respective fields through selected literature. The current performance of implemented machine learning methods is described in comparison to traditional statistical methods. Finally, this chapter discusses potential future perspectives for implementation of machine learning in genomics, proteomics, and drug discovery.

Original language	English
Title of host publication	Machine Learning in Cardiovascular Medicine
Publisher	Elsevier
Pages	325-352
Number of pages	28
ISBN (Electronic)	9780128202739
DOIs	https://doi.org/10.1016/B978-0-12-820273-9.00014-2
Publication status	Published - 1 Jan 2020

Keywords

Cardiology
Cardiovascular disease
Drug discovery
Genomics
Machine learning
Proteomics

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10.1016/B978-0-12-820273-9.00014-2

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Machine learning in cardiovascular genomics, proteomics, and drug discovery

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