Abstract
Cell images provide a multitude of phenotypic information, which in its entirety the human eye can hardly perceive. Automated image analysis and machine learning approaches enable the unbiased identification and analysis of cellular mechanisms and associated pathological effects. This protocol describes a customized image analysis pipeline that detects and quantifies changes in the localization of E-Cadherin and the morphology of adherens junctions using image-based measurements generated by CellProfiler and the machine learning functionality of CellProfiler Analyst.
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Acknowledgements
We thank Elena von Coburg for input and comments on the manuscript. This work was supported by an internal BfR research funding program (Sonderforschungsprojekt 1322-683).
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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
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Kornhuber, M., Dunst, S. (2022). Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst. In: Zi, Z., Liu, X. (eds) TGF-Beta Signaling. Methods in Molecular Biology, vol 2488. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2277-3_14
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DOI: https://doi.org/10.1007/978-1-0716-2277-3_14
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Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2276-6
Online ISBN: 978-1-0716-2277-3
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