| Title: | Automated analysis of high-content microscopy data with deep learning |
| Author(s): | Kraus OZ; Grys BT; Ba J; Chong Y; Frey BJ; Boone C; Andrews BJ; |
| Address: | "Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Cellular Pharmacology, Discovery Sciences, Janssen Pharmaceutical Companies, Johnson & Johnson, Beerse, Belgium. Canadian Institute for Advanced Research, Program on Genetic Networks, Toronto, ON, Canada. Canadian Institute for Advanced Research, Program on Learning in Machines & Brains, Toronto, ON, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada charlie.boone@utoronto.ca brenda.andrews@utoronto.ca" |
| ISSN/ISBN: | 1744-4292 (Electronic) 1744-4292 (Linking) |
| 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" |
| Keywords: | "Machine Learning Microscopy Neural Networks, Computer Saccharomyces cerevisiae/metabolism/*ultrastructure Saccharomyces cerevisiae Proteins/*metabolism Systems Biology/*methods Saccharomyces cerevisiae deep learning high-content screening image analysis;" |
| Notes: | "MedlineKraus, Oren Z Grys, Ben T Ba, Jimmy Chong, Yolanda Frey, Brendan J Boone, Charles Andrews, Brenda J eng R01 HG005853/HG/NHGRI NIH HHS/ CIHR/Canada England 2017/04/20 Mol Syst Biol. 2017 Apr 18; 13(4):924. doi: 10.15252/msb.20177551" |
