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Application of bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics
Furxhi, Irini; Murphy, Finbarr; Poland, Craig A.; Sheehan, Barry; Mullins, Martin; Mantecca, Paride
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.
Keyword(s): Bayesian networks; machine learning; in vitro; transcriptomics; nanoparticles; information gain
Publication Date:
2019
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): Nanotoxicology;
720851
Publisher(s): Taylor & Francis
First Indexed: 2019-06-15 06:25:34 Last Updated: 2019-06-15 06:25:34