A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri

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

dc.contributor.author	Melagraki, G	en
dc.contributor.author	Afantitis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Igglessi-Markopoulou, O	en
dc.contributor.author	Alexandridis, A	en
dc.date.accessioned	2014-03-01T01:23:27Z
dc.date.available	2014-03-01T01:23:27Z
dc.date.issued	2006	en
dc.identifier.issn	1381-1991	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16975
dc.subject	Neural network	en
dc.subject	QSTR	en
dc.subject	RBF architecture	en
dc.subject	Toxicity	en
dc.subject	Vibrio fischeri	en
dc.subject.classification	Biochemistry & Molecular Biology	en
dc.subject.classification	Chemistry, Applied	en
dc.subject.classification	Chemistry, Medicinal	en
dc.subject.classification	Chemistry, Multidisciplinary	en
dc.subject.other	2 chloro 4 nitroaniline	en
dc.subject.other	2 chlorobenzyl cyanide	en
dc.subject.other	2,4 dichloroaniline	en
dc.subject.other	2,4 dichlorophenol	en
dc.subject.other	2,4 dinitroaniline	en
dc.subject.other	2,4,6 trichloroaniline	en
dc.subject.other	2,6 dichloroaniline	en
dc.subject.other	3 chloro 4 uoroaniline	en
dc.subject.other	3 chlorobenzaldehyde	en
dc.subject.other	3 nitroaniline	en
dc.subject.other	3,4 dichloroaniline	en
dc.subject.other	3,4 dichlorobenzaldehyde	en
dc.subject.other	3,4 dichlorobenzonitrile	en
dc.subject.other	4 bromoaniline	en
dc.subject.other	4 chloroaniline	en
dc.subject.other	4 chlorobenzaldehyde	en
dc.subject.other	4 chlorobenzonitrile	en
dc.subject.other	4 chlorobenzyl cyanide	en
dc.subject.other	4 chlorophenol	en
dc.subject.other	4 cholorobenzyl chloride	en
dc.subject.other	4 nitroaniline	en
dc.subject.other	4 nitrophenol	en
dc.subject.other	aniline	en
dc.subject.other	diphenylamine	en
dc.subject.other	hexachloroethane	en
dc.subject.other	organic compound	en
dc.subject.other	ortho cresol	en
dc.subject.other	pentachlorophenol	en
dc.subject.other	phenol	en
dc.subject.other	resorcinol	en
dc.subject.other	unclassified drug	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	external validity	en
dc.subject.other	lipophilicity	en
dc.subject.other	methodology	en
dc.subject.other	priority journal	en
dc.subject.other	statistical analysis	en
dc.subject.other	toxicity	en
dc.subject.other	Vibrio fischeri	en
dc.subject.other	Models, Chemical	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Organic Chemicals	en
dc.subject.other	Quantitative Structure-Activity Relationship	en
dc.subject.other	Toxicology	en
dc.subject.other	Vibrio fischeri	en
dc.title	A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11030-005-9008-y	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11030-005-9008-y	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischerifor a heterogeneous set of compounds. © Springer 2006.	en
heal.publisher	SPRINGER	en
heal.journalName	Molecular Diversity	en
dc.identifier.doi	10.1007/s11030-005-9008-y	en
dc.identifier.isi	ISI:000240031400012	en
dc.identifier.volume	10	en
dc.identifier.issue	2	en
dc.identifier.spage	213	en
dc.identifier.epage	221	en