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

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252798

Filename

6252798