Modeling toxicity by using supervised kohonen neural networks.
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.
Mazzatorta
Paolo
P
Istituto Mario Negri, via Eritrea 62, 20157 Milan, Italy. mazzatorta@marionegri.it
Vracko
Marjan
M
Jezierska
Aneta
A
Benfenati
Emilio
E