Active learning strategies for phenotypic profiling of high-content screens

Journal of Biomolecular Screening

Volumn 19, Issue 5, 2014, Pages 685-695

  Smith, Kevin ^a   Horvath, Peter ^a,b  

^a ETH ZURICH   (Switzerland)

^b INSTITUTE OF BIOCHEMISTRY   (Hungary)

Author keywords

Active learning; High content screening; Machine learning; Multiparametric analysis; Phenotypic discovery

Indexed keywords

ACCURACY; ARTICLE; BIOGENESIS; CLASSIFICATION; COST; HIGH CONTENT SCREEN; IMAGE ANALYSIS; LEARNING; MACHINE LEARNING; MICROSCOPY; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; RIBOSOME; RNA INTERFERENCE; SCREENING; SEMLIKI FOREST ALPHAVIRUS; TRAINING; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; HELA CELL LINE; HIGH THROUGHPUT SCREENING; HUMAN; PROBLEM BASED LEARNING; PROCEDURES; SEMLIKI FOREST VIRUS; STATISTICAL MODEL; UUKUNIEMI VIRUS;

RNA; SMALL INTERFERING RNA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HELA CELLS; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MACHINE LEARNING; MICROSCOPY; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE; PROBLEM-BASED LEARNING; RIBOSOMES; RNA; RNA INTERFERENCE; RNA, SMALL INTERFERING; SEMLIKI FOREST VIRUS; UUKUNIEMI VIRUS;

EID: 84902152908     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057114527313     Document Type: Article

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