An Introduction to Machine Learning.
Journal
Clinical pharmacology and therapeutics
ISSN: 1532-6535
Titre abrégé: Clin Pharmacol Ther
Pays: United States
ID NLM: 0372741
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
08
10
2019
accepted:
15
01
2020
pubmed:
5
3
2020
medline:
28
10
2020
entrez:
5
3
2020
Statut:
ppublish
Résumé
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.
Identifiants
pubmed: 32128792
doi: 10.1002/cpt.1796
pmc: PMC7189875
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
871-885Informations de copyright
© 2020 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.
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