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Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427497/ https://www.ncbi.nlm.nih.gov/pubmed/28391243 http://dx.doi.org/10.1534/g3.116.033654 |
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author | Pärnamaa, Tanel Parts, Leopold |
author_facet | Pärnamaa, Tanel Parts, Leopold |
author_sort | Pärnamaa, Tanel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5427497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-54274972017-05-12 Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning Pärnamaa, Tanel Parts, Leopold G3 (Bethesda) Investigations 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. Genetics Society of America 2017-04-08 /pmc/articles/PMC5427497/ /pubmed/28391243 http://dx.doi.org/10.1534/g3.116.033654 Text en Copyright © 2017 Parnamaa and Parts http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigations Pärnamaa, Tanel Parts, Leopold Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title | Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title_full | Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title_fullStr | Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title_full_unstemmed | Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title_short | Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning |
title_sort | accurate classification of protein subcellular localization from high-throughput microscopy images using deep learning |
topic | Investigations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427497/ https://www.ncbi.nlm.nih.gov/pubmed/28391243 http://dx.doi.org/10.1534/g3.116.033654 |
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