Title: Validation of Counter Propagation Neural Network Models for Predictive Toxicology According to the OECD Principles. A Case Study
Authors: VRACKO GROBELSEK MarjanBANDELJ VinkoBARBIERI PierluigiBENFENATI EmilioCHAUDHRY QuasimCRONIN MARKDEVILLERS JamesGALLEGOS SALINER AnaGINI GuiseppinaGRAMATICA PaolaHELMA ChristophNEAGU DanielNETZEVA TATIANAPAVAN MANUELATIER GRACERANDIC MilanWORTH ANDREWTSAKOVSKA Ivanka
Citation: SAR AND QSAR IN ENVIRONMENTAL RESEARCH vol. 17 no. 3 p. 265-284
Publisher: TAYLOR & FRANCIS INC
Publication Year: 2006
JRC Publication N°: JRC31712
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC31712
Type: Articles in Journals
Abstract: OECD adopted five principles for validation of QSAR models used for regulatory purposes. We present Kohonen neural networks and counter propagation neural networks, which are often used for QSAR modeling, in the light of these principles. As a case study we present a counter propagation network built on 541 compounds for modeling of toxicity toward fish fathead minnow.
JRC Institute:Institute for Health and Consumer Protection

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