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G3 (Bethesda). 2017 May 5;7(5):1385-1392. doi: 10.1534/g3.116.033654.

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

Author information

1
Institute of Computer Science, University of Tartu, 50409, Estonia.
2
Institute of Computer Science, University of Tartu, 50409, Estonia leopold.parts@sanger.ac.uk.
3
Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, United Kingdom.

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.

KEYWORDS:

deep learning; high-content screening; machine learning; microscopy; yeast

PMID:
28391243
PMCID:
PMC5427497
DOI:
10.1534/g3.116.033654
[Indexed for MEDLINE]
Free PMC Article

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