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Clustering and classification techniques to assess aquatic toxicity
Gini, G.; Benfenati, E.; Boley, D.;
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Volume 1,
30 Aug.-1 Sept. 2000
Page(s):166
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172 vol.1
Abstract:
The goal of toxicity prediction is to describe the relationship between chemical properties, on the one hand, and biological and toxicological processes, on the other. Knowledge about the causes of toxicity is incomplete. No single property can satisfy the requirement to model the toxic activity. In this study, we consider a different method, viz. building up models that are useful for aquatic toxicity prediction. Our study is in the tradition of SAR (structure-activity relationship) and QSAR (quantitative SAR) methods, but it tries to predict a category. Due to the variability of the toxicity phenomenon, classification methods may have advantages, because they refer to intervals of the observed toxic effect. Furthermore, the classification of compounds according to their toxicity has a direct application to the regulation of chemicals. In this paper, we report results obtained from the preparation and study of a data set of different classes of chemicals. Starting from recursive partitioning algorithms, we test their results against clustering and classifiers
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