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Nanotoxicology Volume 10, 2016 - Issue 7
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Original Article

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

Ceyda OkselSchool of Chemical and Process Engineering, University of Leeds, Leeds, UK,
,
David A. WinklerCSIRO Manufacturing Flagship, Clayton South, MDC, Melbourne, Australia, ;Monash Institute of Pharmaceutical Sciences, Parkville, Melbourne, Australia, ;Latrobe Institute for Molecular Science, Bundoora, Melbourne, Australia, and ;School of Chemical and Physical Sciences, Flinders University, Bedford Park, Adelaide, Australia
,
Cai Y. MaSchool of Chemical and Process Engineering, University of Leeds, Leeds, UK,
,
Terry WilkinsSchool of Chemical and Process Engineering, University of Leeds, Leeds, UK,
&
Xue Z. WangSchool of Chemical and Process Engineering, University of Leeds, Leeds, UK, Correspondence[email protected]
Pages 1001-1012 | Received 23 Dec 2015, Accepted 01 Mar 2016, Published online: 06 Apr 2016

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Aysel Tomak, Selin Cesmeli, Bercem D. Hanoglu, David Winkler & Ceyda Oksel Karakus. (2021) Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity. Nanotoxicology 15:10, pages 1331-1357.
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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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E. Nagihan Kahraman & M. Türker Saçan. (2018) On the prediction of cytotoxicity of diverse chemicals for topminnow (Poeciliopsis lucida) hepatoma cell line, PLHC-1$. SAR and QSAR in Environmental Research 29:9, pages 675-691.
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Agnieszka Gajewicz, Tomasz Puzyn, Katarzyna Odziomek, Piotr Urbaszek, Andrea Haase, Christian Riebeling, Andreas Luch, Muhammad A. Irfan, Robert Landsiedel, Meike van der Zande & Hans Bouwmeester. (2018) Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme. Nanotoxicology 12:1, pages 1-17.
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David A. Winkler. (2020) Role of Artificial Intelligence and Machine Learning in Nanosafety. Small 16:36, pages 2001883.
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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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Etenaldo F. Santiago, Montcharles S. Pontes, Gilberto J. Arruda, Anderson R. L. Caires, Ian Colbeck, Ronald Maldonado-Rodriguez & Renato Grillo. 2020. Nanopesticides. Nanopesticides 69 109 .
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L. Lamon, D. Asturiol, A. Vilchez, R. Ruperez-Illescas, J. Cabellos, A. Richarz & A. Worth. (2019) Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape. Computational Toxicology 9, pages 143-151.
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G. Basei, D. Hristozov, L. Lamon, A. Zabeo, N. Jeliazkova, G. Tsiliki, A. Marcomini & A. Torsello. (2019) Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review. NanoImpact 13, pages 76-99.
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Bhavna Saini & Sumit Srivastava. (2018) Nanotoxicity prediction using computational modelling - review and future directions. IOP Conference Series: Materials Science and Engineering 348, pages 012005.
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A. Gajewicz. (2018) How to judge whether QSAR/read-across predictions can be trusted: a novel approach for establishing a model's applicability domain. Environmental Science: Nano 5:2, pages 408-421.
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Abraham Yosipof, Klimentiy Shimanovich & Hanoch Senderowitz. (2016) Materials Informatics: Statistical Modeling in Material Science. Molecular Informatics 35:11-12, pages 568-579.
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Didier Mathieu. (2016) Physics-Based Modeling of Chemical Hazards in a Regulatory Framework: Comparison with Quantitative Structure–Property Relationship (QSPR) Methods for Impact Sensitivities. Industrial & Engineering Chemistry Research 55:27, pages 7569-7577.
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