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Poster: Imaging and advanced data-analysis for lead identification in cell-based assaysSBS 2006
Advances in screening have enabled the construction of cellular assays in-line with physiological disease models. Such phenotypic assays permit the measurement of compound action on complex biological networks and increase the chance that leads will be effective in the context of animal models, and ultimately in man.
Sophisticated cellular imaging technology, such as Cellomics ArrayScan, identifies cellular structures and tracks translocation of molecules across compartments and changes in cellular phenotype. However, the complex data present a challenge in interpretation and extraction of meaningful information; the bottleneck has been shifted from data collection towards data analysis.
In this case study we demonstrate the effectiveness of Serono’s process integrating cellular imaging with HCS data analysis by Genedata Screener for identification of hit compounds. We discuss the importance of selecting relevant parameters for analysis and finally demonstrate the suitability of machine learning techniques to substantially enhance the hit verification rate.
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