Integrating high-content screening and ligand-target prediction to identify mechanism of action.

By Daniel W. Young, Andreas Bender, Jonathan Hoyt, E. Mcwhinnie, G. Chirn, Charles Y Tao, John A. Tallarico, M. Labow, J. Jenkins, T. Mitchison and 1 more. Nature Chemical Biology, 2008

344 citations recorded by the source corpus.

Subfields: Biophysics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7d1096bc057b6e5e08a6ae821768d4f742d37b77

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Citations in the corpus

Machine learning and computer vision approaches for phenotypic profiling (2017, background): “For example, Young et al. (2008) profiled a library of ∼ 6,500 compounds for cell cycle defects in HeLa cells.”
High-Content Screening for Quantitative Cell Biology. (2016, methodology): “In this screen, all of the parameters were considered independently; other studies have tried to assess the dependencies between parameters using methods such as factor analysis [36].”
Data-analysis strategies for image-based cell profiling (2017, methodology): “This procedure requires at least two replicates of the experiment, a condition usually met for modern morphological profiling experiments.”
Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (2016, methodology): “Other methods to reduce dimensionality or select a subset of features in morphological profiling data include factor analysis 16,17 , stepwise feature selection to remove linear dependencies 25,27 , and SVM (support vector machine) recursive feature elimination 15 .”
Computer vision for high content screening (2016, methodology): “Young et al. (2008) used this approach and found that compounds that induce similar phenotypic profiles also have similar chemical structures.”