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|Title:||A cell profiling framework for modeling drug responses from HCS imaging||Authors:||Ng, A.Y.J.
high-content screening (HCS)
|Issue Date:||Aug-2010||Citation:||Ng, A.Y.J., Rajapakse, J.C., Welsch, R.E., Matsudaira, P.T., Horodincu, V., Evans, J.G. (2010-08). A cell profiling framework for modeling drug responses from HCS imaging. Journal of Biomolecular Screening 15 (7) : 858-868. ScholarBank@NUS Repository. https://doi.org/10.1177/1087057110372256||Abstract:||The authors present an unsupervised, scalable, and interpretable cell profiling framework that is compatible with data gathered from high-content screening. They demonstrate the effectiveness of their framework by modeling drug differential effects of IC-21 macrophages treated with microtubule and actin disrupting drugs. They identify significant features of cell phenotypes for unsupervised learning based on maximum relevancy and minimum redundancy criteria. A 2-stage clustering approach annotates, clusters cells, and then merges them together to form super-clusters. An interpretable cell profile consisting of super-cluster proportions profiled at each drug treatment, concentration, or duration is obtained. Differential changes in super-cluster profiles are the basis for understanding the drugs differential effect and biology. The authors method is validated by significant chi-squared statistics obtained from similar drug-treated super-cluster profiles from a 5-fold cross-validation. In addition, drug profiles of 2 microtubule drugs with equivalent mechanisms of action are statistically similar. Several distinct trends are identified for the 5 cytoskeletal drugs profiled under different conditions. © 2010 Society for Laboratory Automation and Screening.||Source Title:||Journal of Biomolecular Screening||URI:||http://scholarbank.nus.edu.sg/handle/10635/99798||ISSN:||10870571||DOI:||10.1177/1087057110372256|
|Appears in Collections:||Staff Publications|
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