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- Ben T. Grys
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
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- Dara S. Lo
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
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- Nil Sahin
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
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- Oren Z. Kraus
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada 2
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- Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
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- Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
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- Brenda J. Andrews
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada 1
説明
<jats:p>With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.</jats:p>
収録刊行物
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- Journal of Cell Biology
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Journal of Cell Biology 216 (1), 65-71, 2016-12-09
Rockefeller University Press
