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Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.


ABSTRACT: 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.

SUBMITTER: Parnamaa T 

PROVIDER: S-EPMC5427497 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.

Pärnamaa Tanel T   Parts Leopold L  

G3 (Bethesda, Md.) 20170505 5


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 pe  ...[more]

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