title: Modeling toxicity by using supervised kohonen neural networks.
abstract: Counterprogation neural network is shown to be a powerful and suitable tool for the investigation of toxicity. This study mined a data set of 568 chemicals. Two hundred eighty-two objects were used as the training set and 286 as the test set. The final model developed presents high performances on the data set R(2) = 0.83 (R(2) = 0.97 on the training set, R(2) = 0.59 on the test set). This technique distinguishes itself also for the ability to give to the expert two-dimensional maps suitable for the study of the distribution/clustering of the data and the identification of outliers.
authors.affiliation: Istituto Mario Negri, via Eritrea 62, 20157 Milan, Italy. mazzatorta@marionegri.it
authors.forename: Paolo
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: Marjan
authors.initials: M
authors.lastname: Vracko
authors.forename: Aneta
authors.initials: A
authors.lastname: Jezierska
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati