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dc.contributor.authorCenk, N.en_US
dc.contributor.authorBudak, G.en_US
dc.contributor.authorDayanik, S.en_US
dc.contributor.authorSabuncuoglu, I.en_US
dc.date.accessioned2016-02-08T10:59:00Z
dc.date.available2016-02-08T10:59:00Z
dc.date.issued2014en_US
dc.identifier.issn1546-1963
dc.identifier.urihttp://hdl.handle.net/11693/26378
dc.description.abstractIn this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.en_US
dc.language.isoEnglishen_US
dc.source.titleJournal of Computational and Theoretical Nanoscienceen_US
dc.relation.isversionofhttps://doi.org/10.1166/jctn.2014.3348en_US
dc.subjectArtificial Neural Networksen_US
dc.subjectNanomedicineen_US
dc.subjectNanoparticle Uptake Rateen_US
dc.subjectPrediction Modelen_US
dc.subjectTargeted Drug Deliveryen_US
dc.subjectAdvanced technologyen_US
dc.subjectArtificial neural network modelingen_US
dc.subjectCellular uptake efficiencyen_US
dc.subjectDispersion characteristicsen_US
dc.subjectFeedforward backpropagationen_US
dc.subjectNanoparticle uptakesen_US
dc.subjectPrediction modelen_US
dc.subjectTargeted drug deliveryen_US
dc.subjectBackpropagation algorithmsen_US
dc.subjectCell membranesen_US
dc.subjectComputer simulationen_US
dc.subjectDrug deliveryen_US
dc.subjectExperimentsen_US
dc.subjectMathematical modelsen_US
dc.subjectMedical nanotechnologyen_US
dc.subjectNanoparticlesen_US
dc.subjectNeural networksen_US
dc.subjectOptimizationen_US
dc.subjectPolymethyl methacrylatesen_US
dc.subjectCytologyen_US
dc.titleArtificial neural network modeling and simulation of in-vitro nanoparticle-cell interactionsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.departmentDepartment of Managementen_US
dc.citation.spage275en_US
dc.citation.epage282en_US
dc.citation.volumeNumber11en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1166/jctn.2014.3348en_US
dc.publisherAmerican Scientific Publishersen_US
dc.identifier.eissn1546-1963


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