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Publication Citation: Data Quality for High Content Screening

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Author(s): YaShian Li-Baboud; Oleg Aulov; Peter Bajcsy;
Title: Data Quality for High Content Screening
Published: September 03, 2012
Abstract: TO HAVE CONFIDENCE IN THE KNOWLEDGE GLEANED FROM DATA, IT IS ESSENTIAL TO VERIFY THE QUALITY OF THE DATA SOURCES AND TO HAVE A MEANS OF QUANTIFYING THE POTENTIAL UNCERTAINTIES DUE TO THE QUALITY OF DATA FOR MAKING CRITICAL, INTELLIGENT DECISIONS. HIGH CONTENT SCREENING (HCS) IS AN AUTOMATED MICROSCOPY TECHNIQUE ENABLING THE EVALUATION OF SPATIAL AND TEMPORAL EFFECTS ON CELLS FOR DRUG DISCOVERY AND OTHER APPLICATIONS. HIGHER THROUGHPUT OFTEN MEANS COMPROMISING THE IMAGE QUALITY. HOWEVER, SOMETIMES HIGH THROUGHPUT IS REQUIRED TO CAPTURE TRANSIENT MORPHOLOGICAL EFFECTS. UNDERSTANDING HOW TO QUANTIFY AND IMPROVE DATA QUALITY WOULD LEAD TO HIGHER CONFIDENCE IN HCS FINDINGS.
Proceedings: Big Data Workshop
Location: Gaithersburg, MD
Dates: June 13-14, 2012
Keywords: Data Quality; High Content Screening
Research Areas: Data and Informatics, Uncertainty Analysis