Application of Neural Networks in the QSAR Analysis of Percent Effect Biological Data: Comparison with Adaptive Least Squares and Nonlinear Regression Analysis
作者:
M. Wiese,
K.-J. Schaper,
期刊:
SAR and QSAR in Environmental Research
(Taylor Available online 1993)
卷期:
Volume 1,
issue 2-3
页码: 137-152
ISSN:1062-936X
年代: 1993
DOI:10.1080/10629369308028825
出版商: Taylor & Francis Group
关键词: artificial neural network;QSAR;percent effect;adaptive least squares;nonlinear regression
数据来源: Taylor
摘要:
Artificial neural networks (ANN) can be used for the direct QSAR analysis of percent effect biological data, thus avoiding the bias introduced by arbitrarily chosen classes and the loss of information due to prior classification. For two data sets the ANN results are compared with those obtained by adaptive least squares and nonlinear regression analyses. In comparison with the other methods the neural network shows higher predictive power and does not require an explicit equation relating the observed effect to physico-chemical descriptors.
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