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Application of Machine Learning in Microbiology
Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482238/ https://www.ncbi.nlm.nih.gov/pubmed/31057526 http://dx.doi.org/10.3389/fmicb.2019.00827 |
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author | Qu, Kaiyang Guo, Fei Liu, Xiangrong Lin, Yuan Zou, Quan |
author_facet | Qu, Kaiyang Guo, Fei Liu, Xiangrong Lin, Yuan Zou, Quan |
author_sort | Qu, Kaiyang |
collection | PubMed |
description | Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology. |
format | Online Article Text |
id | pubmed-6482238 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64822382019-05-03 Application of Machine Learning in Microbiology Qu, Kaiyang Guo, Fei Liu, Xiangrong Lin, Yuan Zou, Quan Front Microbiol Microbiology Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology. Frontiers Media S.A. 2019-04-18 /pmc/articles/PMC6482238/ /pubmed/31057526 http://dx.doi.org/10.3389/fmicb.2019.00827 Text en Copyright © 2019 Qu, Guo, Liu, Lin and Zou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Qu, Kaiyang Guo, Fei Liu, Xiangrong Lin, Yuan Zou, Quan Application of Machine Learning in Microbiology |
title | Application of Machine Learning in Microbiology |
title_full | Application of Machine Learning in Microbiology |
title_fullStr | Application of Machine Learning in Microbiology |
title_full_unstemmed | Application of Machine Learning in Microbiology |
title_short | Application of Machine Learning in Microbiology |
title_sort | application of machine learning in microbiology |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482238/ https://www.ncbi.nlm.nih.gov/pubmed/31057526 http://dx.doi.org/10.3389/fmicb.2019.00827 |
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