Handling uncertainties in toxicity modelling using a fuzzy filter |
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Authors: | S. Kumar M. Kumar R. Stoll |
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Affiliation: | 1. Institute of Chemistry, University of Rostock , Albert Einstein Str. 3a, D-18059, Rostock, Germany;2. Centre for Life Science Automation , F.-Barnewitz-Str. 8, D-18119, Rostock, Germany;3. Institute of Preventive Medicine, University of Rostock , St.-Georg-Str. 108, D-18055, Rostock, Germany |
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Abstract: | 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: | QSAR models Fuzzy filter Uncertainties Robustness Fuzzy clustering Toxicity |
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