Innovation in Flow Cytometry Analysis: A New Paradigm Delineating Normal or Diseased Bone Marrow Subsets Through Machine Learning

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Published inHemaSphere Vol. 3; no. 2; pp. e173 - n/a
Main Authors Lacombe, Francis, Dupont, Benoît, Lechevalier, Nicolas, Vial, Jean Philippe, Béné, Marie C.
Format Journal Article
LanguageEnglish
Published Wolters Kluwer Health 01.04.2019
Wiley
Subjects
Letter
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ISSN2572-9241
2572-9241
DOI10.1097/HS9.0000000000000173

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Abstract Supplemental Digital Content is available in the text
AbstractList Supplemental Digital Content is available in the text
Author Dupont, Benoît
Lechevalier, Nicolas
Béné, Marie C.
Vial, Jean Philippe
Lacombe, Francis
AuthorAffiliation 2 Beckman Coulter France S.A.S., Villepinte, France
1 Hematology Laboratory, Bordeaux University Hospital, Pessac, France
3 Hematology Biology, Nantes University Hospital, Nantes, France
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  fullname: Vial, Jean Philippe
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  surname: Béné
  fullname: Béné, Marie C.
  email: mariecbene@gmail.com
  organization: Nantes University Hospital
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10.1182/blood.V77.6.1218.1218
10.1002/cyto.b.20484
10.1038/leu.2016.44
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10.1002/cyto.b.21602
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10.1002/eji.201545774
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Notes FL and MCB designed the study, analyzed the data, and wrote the manuscript; BD developed R scripts and processed FlowSOM analyses; FL, NL, and JPV provided data.
www.hemaspherejournal.com
Funding/support: None.
Disclosure: The authors declare no conflict of interest, but it must be mentioned that BD is an employee of Beckman Coulter.
This work was presented at the 2017 ASH meeting
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website
Blood
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2017;130:1421.
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Title Innovation in Flow Cytometry Analysis: A New Paradigm Delineating Normal or Diseased Bone Marrow Subsets Through Machine Learning
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