Learning representations for image-based profiling of perturbations

Abstract Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of t...

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Bibliographic Details

Published in:	Nature Communications
Main Authors:	Nikita Moshkov, Michael Bornholdt, Santiago Benoit, Matthew Smith, Claire McQuin, Allen Goodman, Rebecca A. Senft, Yu Han, Mehrtash Babadi, Peter Horvath, Beth A. Cimini, Anne E. Carpenter, Shantanu Singh, Juan C. Caicedo
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-02-01
Online Access:	https://doi.org/10.1038/s41467-024-45999-1