期刊
NATURE METHODS
卷 9, 期 7, 页码 711-U267出版社
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2046
关键词
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资金
- European Community [241548, 258068]
- European Young Investigator award of the European Science Foundation
- EMBO Young Investigator Programme fellowship
- Swiss National Science Foundation
- SystemsX.ch initiative
- EMBO long-term fellowship
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
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