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Nanotoxicology Volume 13, 2019 - Issue 6
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Articles

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

Irini FurxhiDepartment of Accounting and Finance, Kemmy Business School University of Limerick, Limerick, Ireland; View further author information
,
Finbarr MurphyDepartment of Accounting and Finance, Kemmy Business School University of Limerick, Limerick, Ireland; Correspondence[email protected]
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Craig A. PolandELEGI/Colt Laboratory, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, Scotland; View further author information
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Barry SheehanDepartment of Accounting and Finance, Kemmy Business School University of Limerick, Limerick, Ireland; View further author information
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Martin MullinsDepartment of Accounting and Finance, Kemmy Business School University of Limerick, Limerick, Ireland; View further author information
&
Paride ManteccaDepartment of Earth and Environmental Sciences, Particulate Matter and Health Risk (POLARIS) Research Centre University of Milano Bicocca, Milano, ItalyView further author information
Pages 827-848 | Received 11 Jan 2019, Accepted 09 Mar 2019, Published online: 29 May 2019

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Alireza Banaye Yazdipour, Hoorie Masoorian, Mahnaz Ahmadi, Niloofar Mohammadzadeh & Seyed Mohammad Ayyoubzadeh. (2023) Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review. Nanotoxicology 17:1, pages 62-77.
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Jaeseong Jeong & Jinhee Choi. (2022) Quantitative adverse outcome pathway (qAOP) using bayesian network model on comparative toxicity of multi-walled carbon nanotubes (MWCNTs): safe-by-design approach. Nanotoxicology 16:5, pages 679-694.
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Irini Furxhi, Finbarr Murphy, Martin Mullins, Athanasios Arvanitis & Craig A. Poland. (2020) Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 14:5, pages 612-637.
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Articles from other publishers (22)

Mahnaz Ahmadi, Seyed Mohammad Ayyoubzadeh & Fatemeh Ghorbani-Bidkorpeh. (2023) Toxicity prediction of nanoparticles using machine learning approaches. Toxicology, pages 153697.
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Irini Furxhi, Egon Willighagen, Chris Evelo, Anna Costa, Davide Gardini & Ammar Ammar. (2023) A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability. NanoImpact 31, pages 100475.
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Irini Furxhi, Rossella Bengalli, Giulia Motta, Paride Mantecca, Ozge Kose, Marie Carriere, Ehtsham Ul Haq, Charlie O’Mahony, Magda Blosi, Davide Gardini & Anna Costa. (2023) Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms. ACS Applied Nano Materials 6:5, pages 3948-3962.
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Yaping Liu, Shuang Zhu, Zhanjun Gu & Chunying Chen. 2023. Encyclopedia of Nanomaterials. Encyclopedia of Nanomaterials 718 732 .
Afshin Saadat, Asma Dehghani Varniab & Seyyed Mohammad Madani. (2022) Prediction of the Antibacterial Activity of the Green Synthesized Silver Nanoparticles against Gram Negative and Positive Bacteria by using Machine Learning Algorithms. Journal of Nanomaterials 2022, pages 1-10.
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Valérie Forest. (2022) Experimental and Computational Nanotoxicology—Complementary Approaches for Nanomaterial Hazard Assessment. Nanomaterials 12:8, pages 1346.
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Irini Furxhi. (2022) Health and environmental safety of nanomaterials: O Data, Where Art Thou?. NanoImpact 25, pages 100378.
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Nina Jeliazkova, Eric Bleeker, Richard Cross, Andrea Haase, Gemma Janer, Willie Peijnenburg, Mario Pink, Hubert Rauscher, Claus Svendsen, Georgia Tsiliki, Alex Zabeo, Danail Hristozov, Vicki Stone & Wendel Wohlleben. (2022) How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity. NanoImpact 25, pages 100366.
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Irini Furxhi, Finbarr Murphy, Craig A. Poland, Martin Cunneen & Martin Mullins. (2021) Precaution as a Risk in Data Gaps and Sustainable Nanotechnology Decision Support Systems: a Case Study of Nano-Enabled Textiles Production. NanoEthics 15:3, pages 245-270.
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Olesja Bondarenko, Monika Mortimer, Anne Kahru, Neus Feliu, Ibrahim Javed, Aleksandr Kakinen, Sijie Lin, Tian Xia, Yang Song, Thomas P. Davis, Iseult Lynch, Wolfgang J. Parak, David Tai Leong, Pu Chun Ke, Chunying Chen & Yuliang Zhao. (2021) Nanotoxicology and nanomedicine: The Yin and Yang of nano-bio interactions for the new decade. Nano Today 39, pages 101184.
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Mahsa Mirzaei, Irini Furxhi, Finbarr Murphy & Martin Mullins. (2021) A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles. Nanomaterials 11:7, pages 1774.
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Peng Zhang, Zhiling Guo, Sami Ullah, Georgia Melagraki, Antreas Afantitis & Iseult Lynch. (2021) Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants 7:7, pages 864-876.
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Ceyda Oksel Karakus & David A Winkler. (2021) Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology. Nano Futures 5:2, pages 022002.
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Yuying Jia, Xuan Hou, Zhongwei Wang & Xiangang Hu. (2021) Machine Learning Boosts the Design and Discovery of Nanomaterials. ACS Sustainable Chemistry & Engineering 9:18, pages 6130-6147.
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Nilesh Anantha Subramanian & Ashok Palaniappan. (2021) NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features . ACS Omega 6:17, pages 11729-11739.
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Hung-Jin Huang, Yu-Hsuan Lee, Yung-Ho Hsu, Chia-Te Liao, Yuh-Feng Lin & Hui-Wen Chiu. (2021) Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing. International Journal of Molecular Sciences 22:8, pages 4216.
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Ajay Vikram Singh, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux & Andreas Luch. (2020) Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Advanced Intelligent Systems 2:12, pages 2000084.
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Irini Furxhi & Finbarr Murphy. (2020) Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning. International Journal of Molecular Sciences 21:15, pages 5280.
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Nicoleta Spinu, Mark T. D. Cronin, Steven J. Enoch, Judith C. Madden & Andrew P. Worth. (2020) Quantitative adverse outcome pathway (qAOP) models for toxicity prediction. Archives of Toxicology 94:5, pages 1497-1510.
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Konstantin Pikula, Alexander Zakharenko, Vladimir Chaika, Konstantin Kirichenko, Aristidis Tsatsakis & Kirill Golokhvast. (2020) Risk assessments in nanotoxicology: bioinformatics and computational approaches. Current Opinion in Toxicology 19, pages 1-6.
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Irini Furxhi, Finbarr Murphy, Martin Mullins, Athanasios Arvanitis & Craig A. Poland. (2020) Practices and Trends of Machine Learning Application in Nanotoxicology. Nanomaterials 10:1, pages 116.
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Irini Furxhi, Finbarr Murphy, Martin Mullins & Craig A. Poland. (2019) Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index. Toxicology Letters 312, pages 157-166.
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