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A pilot study that combines RNAi with time-lapse imaging. Several technical hurdles are overcome to give rise to a scalable platform to study mitosis over time. It is the basis for the MitoCheck project http://www.mitocheck.org/.
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In this study a quantitative morphological profiling of perturbed cells leads to the generation of local signaling networks. Genes in these networks act to regulate cell protrusion, adhesion, and tension.
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Bakal C., Aach J., Church G., and Perrimon N. Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316 5832 (2007) 1753-1756. In this study a quantitative morphological profiling of perturbed cells leads to the generation of local signaling networks. Genes in these networks act to regulate cell protrusion, adhesion, and tension.
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The authors present a multivariate method for classifying human cells on the basis of approximately 300 single-cell phenotypic measurements obtained after the application of chemical compounds. They minimize the feature set to roughly 20 informative features for each compound and marker set. This reduction enhances human interpretability and improves detection of cellular phenotypic changes with little loss of classification accuracy.
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The authors present a gene clustering approach based on RNAi-induced phenotypes in the C. elegans embryo. The obtained 'phenoclusters' contain genes with a similar loss-of-function phenotype, show a good correlation with functional predictions, and can, therefore, be used to predict functions of uncharacterized genes.
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Piano F., Schetter A.J., Morton D.G., Gunsalus K.C., Reinke V., Kim S.K., and Kemphues K.J. Gene clustering based on RNAi phenotypes of ovary-enriched genes in C. elegans. Curr Biol 12 22 (2002) 1959-1964. The authors present a gene clustering approach based on RNAi-induced phenotypes in the C. elegans embryo. The obtained 'phenoclusters' contain genes with a similar loss-of-function phenotype, show a good correlation with functional predictions, and can, therefore, be used to predict functions of uncharacterized genes.
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Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline
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