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- Stephen J Goodswen
- School of Life Sciences, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
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- Joel L N Barratt
- Parasitic Diseases Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333, USA
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- Paul J Kennedy
- School of Computer Science, Faculty of Engineering and Information Technology and the Australian Artificial Intelligence Institute, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
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- Alexa Kaufer
- School of Life Sciences, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
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- Larissa Calarco
- School of Life Sciences, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
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- John T Ellis
- School of Life Sciences, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
抄録
<jats:title>ABSTRACT</jats:title> <jats:p>To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.</jats:p>
収録刊行物
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- FEMS Microbiology Reviews
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FEMS Microbiology Reviews 45 (5), 2021-03-16
Oxford University Press (OUP)