Active machine learning-driven experimentation to determine compound effects on protein patterns

eLife

Volumn 5, Issue FEBRUARY2016, 2016, Pages

  Naik, Armaghan W ^a,b   Kangas, Joshua D ^a,b   Sullivan, Devin P ^a,b   Murphy, Robert F ^a,b,c  

^a Carnegie Mellon University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR DISTRIBUTION; EXPERIMENTAL MODEL; HUMAN; HUMAN EXPERIMENT; MACHINE LEARNING; MICROSCOPY; MODEL; PHENOTYPE; PROTEIN ANALYSIS; ROBOTICS; CELL FUNCTION; CHEMISTRY; CYTOSOL; DRUG EFFECTS; FLUORESCENCE IMAGING; HIGH THROUGHPUT SCREENING; LABORATORY AUTOMATION; PRECLINICAL STUDY; PROCEDURES; SUPERVISED MACHINE LEARNING;

PROTEIN;

AUTOMATION, LABORATORY; CELL PHYSIOLOGICAL PHENOMENA; CYTOSOL; DRUG EVALUATION, PRECLINICAL; HIGH-THROUGHPUT SCREENING ASSAYS; MICROSCOPY; OPTICAL IMAGING; PROTEINS; SUPERVISED MACHINE LEARNING;

EID: 84961282066     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.10047     Document Type: Article

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