Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing

dc.contributor.author	Πατρινός, Παναγιώτης Κ.	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Αφαντίτης, Ανδρέας	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Ιγγλέση-Μαρκοπούλου, Όλγα	el
dc.date.accessioned	2015-06-04T13:42:02Z
dc.date.available	2015-06-04T13:42:02Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15061
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Evolutionary Computing
dc.subject	Genetic algorithms
dc.subject	Neural networks
dc.subject	QSAR
dc.subject	Radial basis functions
dc.subject	Simulating Annealing
dc.subject	Εξελικτική υπολογιστική
dc.subject	Γενετικοί Αλγόριθμοι
dc.subject	Νευρωνικά Δίκτυα
dc.subject	Ακτινικές συναρτήσεις βάσεις
dc.subject	Προσομοίωση ανόπτησης
dc.title	Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh92002377
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1016/S1570-7946(04)80110-X
heal.language	en
heal.access	campus
heal.publicationDate	2004
heal.bibliographicCitation	PATRINOS, P.K., ALEXANDRIDIS, A.P., AFANTITIS, A., SARIMVEIS, H.K. & IGGLESSI-MARKOPOULOU, O. (2004). Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing. Computer Aided Chemical Engineering. [online] 18 (C). p. 265-270. Available from: http://www.elsevier.com/[Accessed 03/07/2007]	en
heal.abstract	Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network models. The method is successfully applied to the benchmark Selwood data set.	en
heal.publisher	Elsevier	en
heal.journalName	Computer Aided Chemical Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true