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A rapid, high-throughput, viral infectivity assay using automated brightfield microscopy with machine learning.

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Bibliographic Details
Title:	A rapid, high-throughput, viral infectivity assay using automated brightfield microscopy with machine learning.
Authors:	Dodkins R; ViQi Inc., Santa Barbara, CA, 93117, United States., Delaney JR; ViQi Inc., Santa Barbara, CA, 93117, United States., Overton T; Department of Biological Sciences North Carolina State University Raleigh, NC 27695, United States., Scholle F; Department of Biological Sciences North Carolina State University Raleigh, NC 27695, United States., Frias-De-Diego A; College of Veterinary Medicine, Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC 27695, United States., Crisci E; College of Veterinary Medicine, Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC 27695, United States., Huq N; Melbec Microbiology Ltd, Rossendale, Lancashire, BB4 4QJ, United Kingdom., Jordan I; ProBioGen AG, Goethestr. 54, 13086 Berlin, Germany., Kimata JT; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, United States., Findley T; ViQi Inc., Santa Barbara, CA, 93117, United States., Goldberg IG; ViQi Inc., Santa Barbara, CA, 93117, United States. Electronic address: ilya@viqiai.com.
Source:	SLAS technology [SLAS Technol] 2023 Oct; Vol. 28 (5), pp. 324-333. Date of Electronic Publication: 2023 Jul 13.
Publication Type:	Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
Journal Info:	Publisher: SAGE Publications Country of Publication: United States NLM ID: 101697564 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2472-6311 (Electronic) Linking ISSN: 24726303 NLM ISO Abbreviation: SLAS Technol Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2472-6311
DOI:	10.1016/j.slast.2023.07.003