Diagnostic Potential of Imaging Flow Cytometry

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dc.contributor.author	Vorobyev, Ivan
dc.contributor.author	Barteneva, Natasha S.
dc.date.accessioned	2019-11-01T08:43:03Z
dc.date.available	2019-11-01T08:43:03Z
dc.date.issued	2018-07-01
dc.identifier.citation	Doan, M., Vorobyev, I., Rees, P., Filby, A., Wolkenhauer, O., Goldfeld, A. E., ... Hennig, H. (2018). Diagnostic Potential of Imaging Flow Cytometry. Trends in Biotechnology, 36(7), 649-652. https://doi.org/10.1016/j.tibtech.2017.12.008	en_US
dc.identifier.uri	https://doi.org/10.1016/j.tibtech.2017.12.008
dc.identifier.uri	http://nur.nu.edu.kz/handle/123456789/4281
dc.description.abstract	Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.	en_US
dc.language.iso	en	en_US
dc.publisher	Nazarbayev University School of Sciences and Humanities	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/us/	*
dc.title	Diagnostic Potential of Imaging Flow Cytometry	en_US
dc.type	Article	en_US
workflow.import.source	science