Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14120
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dc.contributor.authorBilgi, Eyüp-
dc.contributor.authorWinkler, David A.-
dc.contributor.authorÖksel Karakuş, Ceyda-
dc.date.accessioned2024-01-06T07:21:25Z-
dc.date.available2024-01-06T07:21:25Z-
dc.date.issued2023-
dc.identifier.issn1061-186X-
dc.identifier.issn1029-2330-
dc.identifier.urihttps://doi.org/10.1080/1061186X.2023.2288995-
dc.identifier.urihttps://hdl.handle.net/11147/14120-
dc.description.abstractThere is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.en_US
dc.description.sponsorshipScientific Research Projects Coordination Unit of Izmir Institute of Technology [2022IYTE-3-0036]en_US
dc.description.sponsorshipThis work was supported by the Scientific Research Projects Coordination Unit of Izmir Institute of Technology (project number: 2022IYTE-3-0036).en_US
dc.language.isoenen_US
dc.publisherTAYLOR & FRANCIS LTDen_US
dc.relation.ispartofJournal of Drug Targetingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectgold nanoparticlesen_US
dc.subjectcellular uptakeen_US
dc.subjectICPen_US
dc.subjectmedical applicationsen_US
dc.subjectDendritic Cellsen_US
dc.subjectParticle-Sizeen_US
dc.subjectSurfaceen_US
dc.subjectShapeen_US
dc.subjectPredictionen_US
dc.subjectToxicityen_US
dc.subjectDeliveryen_US
dc.subjectNanoen_US
dc.titleIdentifying factors controlling cellular uptake of gold nanoparticles by machine learningen_US
dc.typeArticleen_US
dc.typeArticle; Early Accessen_US
dc.authoridWinkler, Dave/0000-0002-7301-6076-
dc.authoridOksel, Ceyda/0000-0001-5282-4114-
dc.institutionauthor-
dc.departmentİzmir Institute of Technologyen_US
dc.identifier.wosWOS:001121031000001en_US
dc.identifier.scopus2-s2.0-85182161989en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1080/1061186X.2023.2288995-
dc.identifier.pmid38009690en_US
dc.authorwosidWinkler, Dave/A-3774-2008-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.openairetypeArticle; Early Access-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.languageiso639-1en-
crisitem.author.dept03.01. Department of Bioengineering-
crisitem.author.dept03.01. Department of Bioengineering-
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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