Efficient framework for automated classification of subcellular patterns in budding yeast

Cytometry Part A

Volumn 75, Issue 11, 2009, Pages 934-940

  Huh, Seungil ^a   Lee, Donghun ^b   Murphy, Robert F ^b,c  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Location proteomics; Machine learning; Pattern recognition; Subcellular location

Indexed keywords

GREEN FLUORESCENT PROTEIN;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; COMPUTER ANALYSIS; CONTROLLED STUDY; FLUORESCENCE ANALYSIS; IMAGE ANALYSIS; IMAGING SYSTEM; PRIORITY JOURNAL; PRODUCTIVITY; PROTEIN DETERMINATION; PROTEIN LOCALIZATION; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; AUTOMATION; CELL SEPARATION; COMPUTATIONAL BIOLOGY; CYTOPLASM; FLUORESCENT DYES; GREEN FLUORESCENT PROTEINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE; SOFTWARE;

EID: 71049155903     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.20793     Document Type: Article

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