title: The use of cluster significance analysis to identify asymmetric QSAR data sets in toxicology. An example with eye irritation data.
abstract: Cluster significance analysis is a tool that allows the identification of "embedded clusters' in QSAR datasets. It is successfully applied to an eye irritation data set to show that these data are indeed asymmetric. The method identifies five parameters that form an embedded cluster of eye irritants amongst non irritants, although full separation is not achieved. This method has considerable potential to identify potential non-linearity in toxicology data sets and for parameter reduction. It is shown also that this can be obtained relatively quickly with an analysis performed on 100,000 subsets containing the same information as an analysis on 1,000,000 subsets.
authors.affiliation: School of Pharmacy and Chemistry, Liverpool John Moores University, UK.
authors.forename: M T
authors.initials: MT
authors.lastname: Cronin
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D005123:Eye / Q000187:drug effects*; D006801:Humans; D007509:Irritants / Q000009:adverse effects*; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D007509:Irritants