Handling uncertainties in toxicity modelling using a fuzzy filter.
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.
Institute of Chemistry, University of Rostock, Albert Einstein Strasse 3a, Rostock, Germany.
S
S
Kumar
M
M
Kumar
R
R
Stoll
U
U
Kragl
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003530:Cyprinidae; D015386:Hazardous Substances / Q000494:pharmacology*; D008954:Models, Biological; D008962:Models, Theoretical; D013329:Structure-Activity Relationship; D014116:Toxicology / Q000379:methods*
D015386:Hazardous Substances