An Introduction to Machine Learning

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 concept fro...

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Veröffentlicht in:	Clinical pharmacology and therapeutics Jg. 107; H. 4; S. 871 - 885
Hauptverfasser:	Badillo, Solveig, Banfai, Balazs, Birzele, Fabian, Davydov, Iakov I., Hutchinson, Lucy, Kam‐Thong, Tony, Siebourg‐Polster, Juliane, Steiert, Bernhard, Zhang, Jitao David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.04.2020
ISSN:	0009-9236, 1532-6535, 1532-6535
Online-Zugang:	Volltext
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Abstract	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 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.
AbstractList	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 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. 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. 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.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.
Author	Banfai, Balazs Kam‐Thong, Tony Birzele, Fabian Siebourg‐Polster, Juliane Davydov, Iakov I. Steiert, Bernhard Badillo, Solveig Hutchinson, Lucy Zhang, Jitao David
Author_xml	– sequence: 1 givenname: Solveig orcidid: 0000-0002-7563-111X surname: Badillo fullname: Badillo, Solveig email: solveig.badillo@roche.com organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 2 givenname: Balazs orcidid: 0000-0003-0422-7977 surname: Banfai fullname: Banfai, Balazs organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 3 givenname: Fabian orcidid: 0000-0001-8744-9561 surname: Birzele fullname: Birzele, Fabian organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 4 givenname: Iakov I. orcidid: 0000-0003-3510-3926 surname: Davydov fullname: Davydov, Iakov I. organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 5 givenname: Lucy orcidid: 0000-0002-3845-5460 surname: Hutchinson fullname: Hutchinson, Lucy organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 6 givenname: Tony orcidid: 0000-0002-5300-9573 surname: Kam‐Thong fullname: Kam‐Thong, Tony organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 7 givenname: Juliane orcidid: 0000-0002-1759-3223 surname: Siebourg‐Polster fullname: Siebourg‐Polster, Juliane organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 8 givenname: Bernhard orcidid: 0000-0002-1398-1624 surname: Steiert fullname: Steiert, Bernhard organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel – sequence: 9 givenname: Jitao David orcidid: 0000-0002-3085-0909 surname: Zhang fullname: Zhang, Jitao David organization: Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32128792$$D View this record in MEDLINE/PubMed
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Copyright	2020 The Authors. published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics. 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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Notes	[Correction added on 6th March, 2020, after first online publication: Author contribution text was added]. Authors in alphabetical order. All authors contributed equally. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
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References	2010; 11 2015; 31 2019; 15 2013; 20 1909; 415–446 2008; 9 2019; 16 2016; 100 2015; 348 2008; 4 2001; 45 2014; 67 1959; 8 1997; 9 1986; 1 1982; 28 1979; 67 2018; 173 2014; 2 2006; 62 2010; 29 2013; 53 2018; 137 2014; 14 2001; 16 2012; 25 2018; 34 2017; 129 1980; 28 1963; 24 2019; 8 2015; 12 2017; 61 2011; 1 2012 2019; 1 1985; 8 2019; 37 2006; 14 2015; 54 2008 1997 1996 2006; 6 1989; 140 1901; 2 2003 2002 2014; 41 2019; 380 2012; 34 2016; 5 2009; 33 2010; 88 2002; 29 2019; 86 1984; 37 2004; 14 2018; 115 2018 2005; 10 2017; 19 2016 2014 2013 2012; 5 2005; 18 2018; 16 2010; 50
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Snippet	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... 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...
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