Document Detail


Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet.
MedLine Citation:
PMID:  10969875     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.
Authors:
I V Tetko; T I Aksenova; V V Volkovich; T N Kasheva; D V Filipov; W J Welsh; D J Livingstone; Villa AEP
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  SAR and QSAR in environmental research     Volume:  11     ISSN:  1062-936X     ISO Abbreviation:  SAR QSAR Environ Res     Publication Date:  2000  
Date Detail:
Created Date:  2000-12-11     Completed Date:  2001-01-04     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9440156     Medline TA:  SAR QSAR Environ Res     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  263-80     Citation Subset:  IM    
Affiliation:
Department of Biomedical Applications, Institute of Bioorganic and Petroleum Chemistry, Kyiv, Ukraine. tetko@bioorganic.kiev.ua
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MeSH Terms
Descriptor/Qualifier:
Internet*
Models, Statistical
Neural Networks (Computer)*
Quantitative Structure-Activity Relationship*
Regression Analysis
Software

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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