期刊
MOLECULAR BIOLOGY OF THE CELL
卷 28, 期 23, 页码 3428-3436出版社
AMER SOC CELL BIOLOGY
DOI: 10.1091/mbc.E17-05-0333
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资金
- European Community's Seventh Framework Programme FP7 [241548, 258068]
- ERC Starting Grant [281198]
- Austrian Science Fund (FWF) Project [SFB F34-06]
- European Research Council (ERC) [281198] Funding Source: European Research Council (ERC)
Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in highcontent screening.
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