Diagnostic Potential of Imaging Flow Cytometry
| 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.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.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.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 |