Machine learning and computer vision approaches for phenotypic profiling

Journal of Cell Biology

Volumn 216, Issue 1, 2017, Pages 65-71

  Grys, Ben T ^a   Lo, Dara S ^a   Sahin, Nil ^a   Kraus, Oren Z ^a,b   Morris, Quaid ^a,b   Boone, Charles ^a   Andrews, Brenda J ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BACK PROPAGATION; BIOINFORMATICS; CELL CYCLE; CELL SELECTION; CLASSIFICATION ALGORITHM; CLASSIFIER; CLUSTER ANALYSIS; COMPUTER; CYTOLOGY; EXTRACTION; GENE EXPRESSION; HUMAN; LINEAR SYSTEM; MACHINE LEARNING; MOLECULAR BIOLOGY; NONLINEAR SYSTEM; PHENOTYPE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN LOCALIZATION; SUPPORT VECTOR MACHINE; ANIMAL; CONFOCAL MICROSCOPY; FLUORESCENCE MICROSCOPY; HIGH THROUGHPUT SCREENING; IMAGE PROCESSING; PROCEDURES; STATISTICAL MODEL;

ANIMALS; CELL BIOLOGY; CLUSTER ANALYSIS; CYTOLOGICAL TECHNIQUES; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MICROSCOPY, CONFOCAL; MICROSCOPY, FLUORESCENCE; MODELS, STATISTICAL; PHENOTYPE;

EID: 85008602558     PISSN: 00219525     EISSN: 15408140     Source Type: Journal    
DOI: 10.1083/jcb.201610026     Document Type: Article

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