Machine Learning: An Overview and Applications in Pharmacogenetics

Overview

Journal Genes (Basel)

Publisher MDPI

Date 2021 Oct 23

PMID 34680905

Citations 11

Authors

Giovanna Cilluffo

Salvatore Fasola

Giuliana Ferrante

Velia Malizia

Laura Montalbano

Stefania La Grutta

Affiliations

Soon will be listed here.

Abstract

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.

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