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dc.contributor.authorBalraadjsing, Surendra
dc.contributor.authorPeijnenburg, Willie J G M
dc.contributor.authorVijver, Martina G
dc.date.accessioned2022-08-15T11:06:24Z
dc.date.available2022-08-15T11:06:24Z
dc.date.issued2022-08-09
dc.identifier.pmid35961453
dc.identifier.doi10.1016/j.chemosphere.2022.135930
dc.identifier.urihttp://hdl.handle.net/10029/626022
dc.language.isoenen_US
dc.rightsCopyright © 2022. Published by Elsevier Ltd.
dc.subjectEcotoxicityen_US
dc.subjectIn silico modelsen_US
dc.subjectIn vivoen_US
dc.subjectMachine learningen_US
dc.subjectMetallic nanoparticlesen_US
dc.subjectScreening risk assessmenten_US
dc.titleExploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity.en_US
dc.typeArticleen_US
dc.identifier.eissn1879-1298
dc.identifier.journalChemosphere 2022;135930en_US
dc.source.journaltitleChemosphere
dc.source.beginpage135930
dc.source.endpage
dc.source.countryEngland


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