期刊
G3-GENES GENOMES GENETICS
卷 7, 期 5, 页码 1385-1392出版社
OXFORD UNIV PRESS INC
DOI: 10.1534/g3.116.033654
关键词
deep learning; high-content screening; machine learning; microscopy; yeast
资金
- European Regional Development Fund through the BioMedIT project
- Estonian Research Council [IUT34-4]
- Wellcome Trust
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.
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