Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst.
| Autor: | Kornhuber M; German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany., Dunst S; German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany. Sebastian.Dunst@bfr.bund.de. |
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| Jazyk: | angličtina |
| Zdroj: | Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2022; Vol. 2488, pp. 207-226. |
| DOI: | 10.1007/978-1-0716-2277-3_14 |
| Abstrakt: | 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. (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Databáze: | MEDLINE |
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