Prediction of toxicity using a novel RBF neural network training methodology

Melagraki, G; Afantitis, A; Makridima, K; Sarimveis, H; Igglessi-Markopoulou, O

dc.contributor.author	Melagraki, G	en
dc.contributor.author	Afantitis, A	en
dc.contributor.author	Makridima, K	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Igglessi-Markopoulou, O	en
dc.date.accessioned	2014-03-01T01:24:52Z
dc.date.available	2014-03-01T01:24:52Z
dc.date.issued	2006	en
dc.identifier.issn	1610-2940	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17485
dc.subject	Neural network	en
dc.subject	QSTR	en
dc.subject	RBF architecture	en
dc.subject	Tetrahymena pyriformis	en
dc.subject	Toxicity	en
dc.subject.classification	Biochemistry & Molecular Biology	en
dc.subject.classification	Biophysics	en
dc.subject.classification	Chemistry, Multidisciplinary	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.other	2,3 dichlorophenol	en
dc.subject.other	2,4,6 trichlorophenol	en
dc.subject.other	guaiacol	en
dc.subject.other	pentachlorophenol	en
dc.subject.other	phenol derivative	en
dc.subject.other	phloroglucinol	en
dc.subject.other	accuracy	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	chemical structure	en
dc.subject.other	controlled study	en
dc.subject.other	methodology	en
dc.subject.other	multiple linear regression analysis	en
dc.subject.other	nonhuman	en
dc.subject.other	physical chemistry	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	quantitative analysis	en
dc.subject.other	statistical significance	en
dc.subject.other	structure analysis	en
dc.subject.other	Tetrahymena pyriformis	en
dc.subject.other	toxicity testing	en
dc.subject.other	validation process	en
dc.subject.other	Animals	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Phenols	en
dc.subject.other	Tetrahymena pyriformis	en
dc.subject.other	Tetrahymena pyriformis	en
dc.title	Prediction of toxicity using a novel RBF neural network training methodology	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00894-005-0032-8	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00894-005-0032-8	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models. © Springer-Verlag 2005.	en
heal.publisher	SPRINGER	en
heal.journalName	Journal of Molecular Modeling	en
dc.identifier.doi	10.1007/s00894-005-0032-8	en
dc.identifier.isi	ISI:000235756300006	en
dc.identifier.volume	12	en
dc.identifier.issue	3	en
dc.identifier.spage	297	en
dc.identifier.epage	305	en