Machine learning applications in systems metabolic engineering

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dc.contributor.authorKim, Gi Baeko
dc.contributor.authorKim, Won Junko
dc.contributor.authorKim, Hyun Ukko
dc.contributor.authorLee, Sang Yupko
dc.date.accessioned2019-12-13T01:20:32Z-
dc.date.available2019-12-13T01:20:32Z-
dc.date.created2019-12-02-
dc.date.created2019-12-02-
dc.date.issued2020-08-
dc.identifier.citationCURRENT OPINION IN BIOTECHNOLOGY, v.64, pp.1 - 9-
dc.identifier.issn0958-1669-
dc.identifier.urihttp://hdl.handle.net/10203/268741-
dc.description.abstractSystems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.-
dc.languageEnglish-
dc.publisherCURRENT BIOLOGY LTD-
dc.titleMachine learning applications in systems metabolic engineering-
dc.typeArticle-
dc.identifier.scopusid2-s2.0-85072703976-
dc.type.rimsART-
dc.citation.volume64-
dc.citation.beginningpage1-
dc.citation.endingpage9-
dc.citation.publicationnameCURRENT OPINION IN BIOTECHNOLOGY-
dc.identifier.doi10.1016/j.copbio.2019.08.010-
dc.contributor.localauthorKim, Hyun Uk-
dc.contributor.localauthorLee, Sang Yup-
dc.description.isOpenAccessN-
dc.type.journalArticleReview-
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CBE-Journal Papers(저널논문)
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