Machine learning improves the precision and robustness of high-content screens: Using nonlinear multiparametric methods to analyze screening results

Journal of Biomolecular Screening

Volumn 16, Issue 9, 2011, Pages 1059-1067

  Horvath, Peter ^a   Wild, Thomas ^b   Kutay, Ulrike ^a   Csucs, Gabor ^a  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Advanced Cell Classifier; machine learning; multiparametric methods; siRNA screening

Indexed keywords

PROTEIN; SMALL INTERFERING RNA;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CASE STUDY; CONTROLLED STUDY; DATA ANALYSIS; FLUORESCENCE; HIGH CONTENT SCREENING; HUMAN; HUMAN CELL; IMAGE ANALYSIS; MACHINE LEARNING; NONLINEAR SYSTEM; PARAMETERS OF MEASUREMENT AND ANALYSIS; PERFORMANCE; PRIORITY JOURNAL; SCREENING;

ARTIFICIAL INTELLIGENCE; HIGH-THROUGHPUT SCREENING ASSAYS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); RNA, SMALL INTERFERING; SINGLE-CELL ANALYSIS;

EID: 80053466067     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057111414878     Document Type: Article

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