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J Cell Biol. 2017 Jan 2;216(1):65-71. doi: 10.1083/jcb.201610026. Epub 2016 Dec 9.

Machine learning and computer vision approaches for phenotypic profiling.

Grys BT1,2, Lo DS1,2, Sahin N1,2, Kraus OZ2,3, Morris Q1,2,3, Boone C4,2, Andrews BJ4,2.

Author information

1
Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
2
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
3
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada.
4
Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada charlie.boone@utoronto.ca brenda.andrews@utoronto.ca.

Abstract

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.

PMID:
27940887
PMCID:
PMC5223612
DOI:
10.1083/jcb.201610026
[Indexed for MEDLINE]
Free PMC Article

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