Title :
A neural network-based prediction model of AR inhibitory activity from a sparse set of compounds
Author :
Parra-Hernandez, R. ; Laxdal, E.M. ; Dimopoulos, N.J. ; Alexiou, Polyxeni
Author_Institution :
Dept. of Electr. & Comput. Eng., Victoria Univ., Victoria, BC, Canada
fDate :
July 31 2005-Aug. 4 2005
Abstract :
In this paper, we present a mechanism to obtain a neural network-based model that predicts an enzyme inhibitory activity of a group of compounds. The mechanism selects the compounds, among a sparse set of, that should be used to obtain models of the inhibitory activity of interest. That is, the mechanism is aimed at the selection of a training set of compounds which ensures that the training of a neural network-based model results in a system capable of generalization.
Keywords :
enzymes; learning (artificial intelligence); neural nets; physiological models; compounds training set; enzyme inhibitory activity; neural network-based prediction model; sparse set; Biochemistry; Biological system modeling; Cancer; Chemical compounds; Databases; Electronic mail; Inhibitors; Neural networks; Power cables; Predictive models;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556280
