Automated analysis of high‐content microscopy data with deep learning
Abstract Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2017-04-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.15252/msb.20177551 |
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| _version_ | 1849738362811842560 |
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| author | Oren Z Kraus Ben T Grys Jimmy Ba Yolanda Chong Brendan J Frey Charles Boone Brenda J Andrews |
| author_facet | Oren Z Kraus Ben T Grys Jimmy Ba Yolanda Chong Brendan J Frey Charles Boone Brenda J Andrews |
| author_sort | Oren Z Kraus |
| collection | DOAJ |
| description | Abstract Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data. |
| format | Article |
| id | doaj-art-7d17f04c8f62472f87fec588070afc4e |
| institution | DOAJ |
| issn | 1744-4292 |
| language | English |
| publishDate | 2017-04-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-7d17f04c8f62472f87fec588070afc4e2025-08-20T03:06:36ZengSpringer NatureMolecular Systems Biology1744-42922017-04-0113411510.15252/msb.20177551Automated analysis of high‐content microscopy data with deep learningOren Z Kraus0Ben T Grys1Jimmy Ba2Yolanda Chong3Brendan J Frey4Charles Boone5Brenda J Andrews6Department of Electrical and Computer Engineering, University of TorontoDonnelly Centre for Cellular and Biomolecular Research, University of TorontoDepartment of Electrical and Computer Engineering, University of TorontoCellular Pharmacology, Discovery Sciences, Janssen Pharmaceutical Companies, Johnson & JohnsonDepartment of Electrical and Computer Engineering, University of TorontoDonnelly Centre for Cellular and Biomolecular Research, University of TorontoDonnelly Centre for Cellular and Biomolecular Research, University of TorontoAbstract Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.https://doi.org/10.15252/msb.20177551deep learninghigh‐content screeningimage analysismachine learningSaccharomyces cerevisiae |
| spellingShingle | Oren Z Kraus Ben T Grys Jimmy Ba Yolanda Chong Brendan J Frey Charles Boone Brenda J Andrews Automated analysis of high‐content microscopy data with deep learning Molecular Systems Biology deep learning high‐content screening image analysis machine learning Saccharomyces cerevisiae |
| title | Automated analysis of high‐content microscopy data with deep learning |
| title_full | Automated analysis of high‐content microscopy data with deep learning |
| title_fullStr | Automated analysis of high‐content microscopy data with deep learning |
| title_full_unstemmed | Automated analysis of high‐content microscopy data with deep learning |
| title_short | Automated analysis of high‐content microscopy data with deep learning |
| title_sort | automated analysis of high content microscopy data with deep learning |
| topic | deep learning high‐content screening image analysis machine learning Saccharomyces cerevisiae |
| url | https://doi.org/10.15252/msb.20177551 |
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