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Application of bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

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dc.contributor.author Furxhi, Irini
dc.contributor.author Murphy, Finbarr
dc.contributor.author Poland, Craig A.
dc.contributor.author Sheehan, Barry
dc.contributor.author Mullins, Martin
dc.contributor.author Mantecca, Paride
dc.date.accessioned 2019-06-13T08:14:59Z
dc.date.available 2019-06-13T08:14:59Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10344/7885
dc.description peer-reviewed en_US
dc.description.abstract Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions. en_US
dc.language.iso eng en_US
dc.publisher Taylor & Francis en_US
dc.relation.ispartofseries Nanotoxicology;
dc.subject Bayesian networks en_US
dc.subject machine learning en_US
dc.subject in vitro en_US
dc.subject transcriptomics en_US
dc.subject nanoparticles en_US
dc.subject information gain en_US
dc.title Application of bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1080/17435390.2019.1595206
dc.contributor.sponsor ERC en_US
dc.contributor.sponsor Colt Foundation en_US
dc.contributor.sponsor H2020 en_US
dc.relation.projectid 720851 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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