Title :
An improved neural network ensemble model of Aldose Reductase inhibitory activity
Author :
Hedayati, B. Keshavarz ; Parra-Hernandez, R. ; Laxdal, E.M. ; Dimopoulos, N.J. ; Alexiou, P. ; Demopoulos, V.J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, Canada
Abstract :
In this paper, we improve the results based on a Neural Network-based model that predicts an enzyme (Aldose Reductase) inhibitory activity of a group of compounds. The improvement is due to the judicial selection of ensembles of trained Neural Networks to contribute to the final model. The method is validated on a family of compounds that is different from the families which were used in the training of the model. The results confirm an accurate, chemical-family-independent method that can predict Aldose Reductase inhibitory activity with excellent accuracy.
Keywords :
environmental legislation; enzymes; inhibitors; learning (artificial intelligence); neural nets; Aldose Reductase inhibitory activity; chemical-family-independent method; enzyme prediction; judicial selection; neural network ensemble model; trained neural networks; Artificial neural networks; Compounds; Mathematical model; Predictive models; Sensitivity; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252798
