A deep learning and novelty detection framework for rapid phenotyping in high-content screening

Molecular Biology of the Cell

Volumn 28, Issue 23, 2017, Pages 3428-3436

  Sommer, Christoph ^a   Hoefler, Rudolf ^a   Samwer, Matthias ^a   Gerlich, Daniel W ^a  

^a ERICH SCHMID INSTITUTE OF MATERIALS SCIENCE   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOANALYSIS; CELL NUCLEUS; CELL POPULATION; CELL SCREENING; CELL STRUCTURE; CONTROLLED STUDY; DNA DAMAGE; DNA REPAIR; FEMALE; HELA CELL LINE; HUMAN; HUMAN CELL; HUMAN GENOME; MACHINE LEARNING; MITOSIS; PHENOTYPE; PRIORITY JOURNAL; SOFTWARE; SUPPORT VECTOR MACHINE; ALGORITHM; ANIMAL; BIOLOGICAL VARIATION; GENETICS; HIGH THROUGHPUT SCREENING; MATHEMATICAL COMPUTING; PROCEDURES; STATISTICS; STATISTICS AND NUMERICAL DATA;

ALGORITHMS; ANIMALS; BIOLOGICAL VARIATION, POPULATION; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MACHINE LEARNING; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PHENOTYPE; SOFTWARE; STATISTICS AS TOPIC;

EID: 85033496825     PISSN: 10591524     EISSN: 19394586     Source Type: Journal    
DOI: 10.1091/mbc.E17-05-0333     Document Type: Article

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