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dc.contributor.authorFortino, V
dc.contributor.authorKinaret, PAS
dc.contributor.authorFratello, M
dc.contributor.authorSerra, A
dc.contributor.authorSaarimäki, LA
dc.contributor.authorGallud, A
dc.contributor.authorGupta, G
dc.contributor.authorVales, G
dc.contributor.authorCorreia, M
dc.contributor.authorRasool, O
dc.contributor.authorYtterberg, J
dc.contributor.authorMonopoli, M
dc.contributor.authorSkoog, T
dc.contributor.authorRitchie, P
dc.contributor.authorMoya, S
dc.contributor.authorVázquez-Campos, S
dc.contributor.authorHandy, Richard
dc.contributor.authorGrafström, R
dc.contributor.authorTran, L
dc.contributor.authorZubarev, R
dc.contributor.authorLahesmaa, R
dc.contributor.authorDawson, K
dc.contributor.authorLoeschner, K
dc.contributor.authorLarsen, EH
dc.contributor.authorKrombach, F
dc.contributor.authorNorppa, H
dc.contributor.authorKere, J
dc.contributor.authorSavolainen, K
dc.contributor.authorAlenius, H
dc.contributor.authorFadeel, B
dc.contributor.authorGreco, D
dc.date.accessioned2022-08-04T16:26:00Z
dc.date.issued2022-07-01
dc.identifier.issn2041-1723
dc.identifier.issn2041-1723
dc.identifier.other3798
dc.identifier.urihttp://hdl.handle.net/10026.1/19498
dc.description.abstract

<jats:title>Abstract</jats:title><jats:p>There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.</jats:p>

dc.format.extent3798-
dc.format.mediumElectronic
dc.languageen
dc.language.isoeng
dc.publisherNature Research
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBiomarkers
dc.subjectNanostructures
dc.subjectRNA, Messenger
dc.titleBiomarkers of nanomaterials hazard from multi-layer data
dc.typejournal-article
dc.typeJournal Article
dc.typeResearch Support, Non-U.S. Gov't
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000819790100014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue1
plymouth.volume13
plymouth.publication-statusPublished online
plymouth.journalNature Communications
dc.identifier.doi10.1038/s41467-022-31609-5
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA06 Agriculture, Veterinary and Food Science
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeEngland
dcterms.dateAccepted2022-06-17
dc.rights.embargodate2022-8-5
dc.identifier.eissn2041-1723
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1038/s41467-022-31609-5
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2022-07-01
rioxxterms.typeJournal Article/Review
plymouth.funderNANOSOLUTIONS Biological Foundation for the Safety Classification of Engineered Nanomaterials (ENM): Systems Biology Approaches to Understand::European Commission FP7


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