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 R2 = 0.83 (R2 = 0.97 on the training set, R2 = 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.
|Number of pages||8|
|Journal||Journal of Chemical Information and Computer Sciences|
|Publication status||Published - Mar 2003|
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems