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Access through your institution Buy or subscribe First, the authors used a cellular-proliferation assay that measured 36 nuclear cytological features to screen a chemically diverse library
(that included known bioactives) of more than 6,000 compounds. Factor analysis — a well-defined method for analysing multidimensional data sets that allows data reduction and quantifies
common factors or phenotypes — was then introduced to mine the data sets. This enabled the cytological features to be mapped into six underlying attributes such as nuclear size, nuclear
shape and DNA replication. By calculating mean response scores for each factor for each compound, 211 hits were identified, which were then profiled for biological activity using
hierarchical clustering of the factor scores. This revealed seven primary clusters, termed phenotypes, which could be interpreted as having biological meaning, such as mitotic arrest and
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et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. _Nature Chem. Biol._ 4, 59–68 (2008) Article CAS Google Scholar Download
references Authors * Charlotte Harrison View author publications You can also search for this author inPubMed Google Scholar RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS
ARTICLE CITE THIS ARTICLE Harrison, C. Integrating information. _Nat Rev Drug Discov_ 7, 121 (2008). https://doi.org/10.1038/nrd2522 Download citation * Issue Date: February 2008 * DOI:
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