title: Tuning neural and fuzzy-neural networks for toxicity modeling.
abstract: The need for general reliable models for predicting toxicity has led to the use of artificial intelligence. We applied neural and fuzzy-neural networks with the QSAR approach. We underline how the networks have to be tuned on the data sets generally involved in modeling toxicity. This study was conducted on 562 organic compounds in order to establish models for predictive the acute toxicity in fish.
authors.affiliation: Istituto Mario Negri, via Eritrea 62, Milan, Italy. mazzatorta@marionegri.it
authors.forename: P
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: E
authors.initials: E
authors.lastname: Benfenati
authors.forename: C-D
authors.initials: CD
authors.lastname: Neagu
authors.forename: G
authors.initials: G
authors.lastname: Gini
mesh_terms: D000818:Animals; D003530:Cyprinidae; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D008954:Models, Biological; D008956:Models, Chemical; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D018675:Toxicity Tests / Q000379:methods*
chemical_list: D009930:Organic Chemicals