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Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.

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Title: Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.
Authors: Jeong IJ; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea., Hong JK; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea., Bae YJ; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea., Lee TK; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea.
Source: Cytometry. Part A : the journal of the International Society for Analytical Cytology [Cytometry A] 2025 Mar; Vol. 107 (3), pp. 203-213. Date of Electronic Publication: 2025 Mar 10.
Publication Type: Journal Article
Journal Info: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 101235694 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-4930 (Electronic) Linking ISSN: 15524922 NLM ISO Abbreviation: Cytometry A Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.
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  Data: <searchLink fieldCode="AU" term="%22Jeong+IJ%22">Jeong IJ</searchLink>; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Hong+JK%22">Hong JK</searchLink>; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Bae+YJ%22">Bae YJ</searchLink>; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+TK%22">Lee TK</searchLink>; Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101235694%22">Cytometry. Part A : the journal of the International Society for Analytical Cytology</searchLink> [Cytometry A] 2025 Mar; Vol. 107 (3), pp. 203-213. <i>Date of Electronic Publication: </i>2025 Mar 10.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley-Liss%22">Wiley-Liss </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101235694 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1552-4930 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2215524922%22">15524922 </searchLink><i>NLM ISO Abbreviation: </i>Cytometry A <i>Subsets: </i>MEDLINE
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        Value: 10.1002/cyto.a.24923
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      – Code: eng
        Text: English
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        StartPage: 203
    Titles:
      – TitleFull: Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.
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            NameFull: Jeong IJ
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            NameFull: Hong JK
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            NameFull: Bae YJ
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            NameFull: Lee TK
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            – D: 01
              M: 03
              Text: 2025 Mar
              Type: published
              Y: 2025
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              Value: 1552-4930
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              Value: 107
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            – TitleFull: Cytometry. Part A : the journal of the International Society for Analytical Cytology
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