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Handling uncertainties in toxicity modelling using a fuzzy filter

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A fundamental concern in the Quantitative Structure-Activity Relationship approach to toxicity evaluation is the generalization of the model over a wide range of compounds. The data driven modelling of toxicity, due to the complex and ill-defined nature of eco-toxicological systems, is an uncertain process. The development of a toxicity predicting model without considering uncertainties may produce a model with a low generalization performance. This study presents a novel approach to toxicity modelling that handles the involved uncertainties using a fuzzy filter, and thus improves the generalization capability of the model. The method is illustrated by considering a data set dealing with the fathead minnow (Pimephales promelas) toxicity of 568 organic compounds.
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Keywords: Fuzzy clustering; Fuzzy filter; QSAR models; Robustness; Toxicity; Uncertainties

Document Type: Research Article

Affiliations: 1: Institute of Chemistry, University of Rostock, Rostock, Germany 2: Centre for Life Science Automation, Rostock, Germany 3: Institute of Preventive Medicine, University of Rostock, Rostock, Germany

Publication date: December 1, 2007

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