title: Machine learning and computer vision approaches for phenotypic profiling.
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.
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
authors.forename: Ben T
authors.identifier: http://orcid.org/0000-0002-4888-8729
authors.initials: BT
authors.lastname: Grys
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
authors.forename: Dara S
authors.identifier: http://orcid.org/0000-0001-9206-8384
authors.initials: DS
authors.lastname: Lo
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
authors.forename: Nil
authors.identifier: http://orcid.org/0000-0001-6285-5854
authors.initials: N
authors.lastname: Sahin
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada.
authors.forename: Oren Z
authors.identifier: http://orcid.org/0000-0002-6328-9492
authors.initials: OZ
authors.lastname: Kraus
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada.
authors.forename: Quaid
authors.initials: Q
authors.lastname: Morris
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada charlie.boone@utoronto.ca brenda.andrews@utoronto.ca.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
authors.forename: Charles
authors.initials: C
authors.lastname: Boone
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada charlie.boone@utoronto.ca brenda.andrews@utoronto.ca.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
authors.forename: Brenda J
authors.identifier: http://orcid.org/0000-0001-6427-6493
authors.initials: BJ
authors.lastname: Andrews
mesh_terms: D000818:Animals; D003585:Cell Biology; D016000:Cluster Analysis; D003584:Cytological Techniques; D057166:High-Throughput Screening Assays; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D018613:Microscopy, Confocal / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D015233:Models, Statistical; D010641:Phenotype