Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach

Varsou, Dimitra-Danai; Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos

dc.contributor.author	Varsou, Dimitra-Danai
dc.contributor.author	Afantitis, Antreas
dc.contributor.author	Melagraki, Georgia
dc.contributor.author	Sarimveis, Haralambos
dc.date.accessioned	2021-09-01T12:44:43Z
dc.date.available	2021-09-01T12:44:43Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/53767
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21465
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/gr/	*
dc.subject	read-across	en
dc.title	Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach	en
heal.type	journalArticle
heal.classification	nanoinformatics	en
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2019-07-09
heal.bibliographicCitation	Dimitra-Danai Varsou, Antreas Afantitis, Georgia Melagraki and Haralambos Sarimveis, Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach, Nanoscale Advances, 2019, 1, 3485-3498.	el
heal.abstract	In the present study, a novel read-across methodology for the prediction of toxicity related end-points of engineered nanomaterials (ENMs) is developed. The proposed method lies in the interface between the two main read-across approaches, namely the analogue and the grouping methods, and can employ a single criterion or multiple criteria for defining similarities among ENMs. The main advantage of the proposed method is that there is no need of defining a prior read-across hypothesis. Based on the formulation and the solution of a mathematical optimization problem, the method searches over a space of alternative hypotheses, and determines the one providing the most accurate read-across predictions. The procedure is automated and only two parameters are user-defined: the balance between the level of predictive accuracy and the number of predicted samples, and the similarity criteria, which define the neighbors of a target ENM.	el
heal.sponsor	Onassis Foundation	en
heal.publisher	Royal Society of Chemistry	en
heal.journalName	Nanoscale Advances	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false