Journal
NATURE METHODS
Volume 9, Issue 7, Pages 711-U267Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2046
Keywords
-
Categories
Funding
- 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
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available