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• W3138777723 abstract "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." @default.
• W3138777723 created "2021-03-29" @default.
• W3138777723 creator A5007566988 @default.
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• W3138777723 creator A5080194823 @default.
• W3138777723 creator A5082539529 @default.
• W3138777723 date "2021-03-16" @default.
• W3138777723 modified "2023-10-16" @default.
• W3138777723 title "Machine learning and applications in microbiology" @default.
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• W3138777723 doi "https://doi.org/10.1093/femsre/fuab015" @default.
• W3138777723 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8498514" @default.
• W3138777723 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33724378" @default.
• W3138777723 hasPublicationYear "2021" @default.

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