Label‐Free Identification of White Blood Cells Using Machine Learning-Reference-Cited by-同舟云学术

Label‐Free Identification of White Blood Cells Using Machine Learning

Published:2019-05-13 Issue:8 Volume:95 Page:836-842
ISSN:1552-4922
Container-title:Cytometry Part A
language:en
Short-container-title:Cytometry

Author:

Nassar Mariam¹,Doan Minh²,Filby Andrew³,Wolkenhauer Olaf¹⁴,Fogg Darin K.⁵,Piasecka Justyna⁶,Thornton Catherine A.⁶,Carpenter Anne E.²,Summers Huw D.⁶,Rees Paul⁶,Hennig Holger¹²

Affiliation:

1. Department of Systems Biology & Bioinformatics University of Rostock 18051 Rostock Germany

2. Imaging Platform at the Broad Institute of Harvard and MIT 415 Main St, Cambridge Massachusetts 02142

3. Flow Cytometry Core Facility, Faculty of Medical Sciences Newcastle University Newcastle upon Tyne NE1 7RU UK

4. Stellenbosch Institute for Advanced Study (STIAS) Stellenbosch South Africa

5. Autograph Biosciences, Inc. Montreal Quebec Canada

6. Centre for Nanohealth, Swansea University Singleton Park, Swansea SA2 8PP UK

Funder

Biotechnology and Biological Sciences Research Council

Foundation for the National Institutes of Health

National Science Foundation

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.a.23794

Reference18 articles.

1. Mass Cytometry: Single Cells, Many Features

2. Label-free cell cycle analysis for high-throughput imaging flow cytometry

3. An open-source solution for advanced imaging flow cytometry data analysis using machine learning

4. Diagnostic Potential of Imaging Flow Cytometry

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