Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish

References

• Abraham, M. H. 1993. “Scales of Solute Hydrogen-Bonding: their Construction and Application to Physicochemical and Biochemical Processes.” Chemical Society Reviews 22 (2): 73–83. doi:10.1039/cs9932200073.
   Web of Science ®Google Scholar
• Alexander, D. L. J., A. Tropsha, and D. A. Winkler. 2015. “Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.” Journal of Chemical Information and Modeling 55 (7): 1316–1322. doi:10.1021/acs.jcim.5b00206.
   PubMed Web of Science ®Google Scholar
• Altman, N. S. 1992. “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression.” The American Statistician 46 (3): 175–185. doi:10.1080/00031305.1992.10475879.
   Web of Science ®Google Scholar
• Ambroise, C., and G. J. McLachlan. 2002. “Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data.” Proceedings of the National Academy of Sciences of the United States of America 99 (10): 6562–6566. doi:10.1073/pnas.102102699.
   PubMed Web of Science ®Google Scholar
• Andreescu, S., M. Ornatska, J. S. Erlichman, A. Estevez, and J. C. Leiter. 2012. “Biomedical Applications of Metal Oxide Nanoparticles.” In Fine Particles in Medicine and Pharmacy, edited by E. Matijević. Boston, MA: Springer. doi:10.1007/978-1-4614-0379-1_3.
   Google Scholar
• Asharani, P. V., Y. L. Wu, Z. Gong, and S. Valiyaveettil. 2008. “Toxicity of Silver Nanoparticles in Zebrafish Models.” Nanotechnology 19 (25): 255102. doi:10.1088/0957-4484/19/25/255102.
   PubMed Web of Science ®Google Scholar
• Avdesh, A., M. Chen, M. T. Martin-Iverson, A. Mondal, D. Ong, S. Rainey-Smith, K. Taddei, et al. 2012. “Regular Care and Maintenance of a Zebrafish (Danio rerio) Laboratory: An Introduction.” Journal of Visualized Experiments 69: e4196. doi:10.3791/4196.
   Google Scholar
• Bache, S. M., and H. Wickham. 2014. “magrittr: A forward-pipe operator for R.” R package version 1.5. https://CRAN.R-project.org/package=magrittr.
   Google Scholar
• Baer, Donald R., Mark H. Engelhard, Grant E. Johnson, Julia Laskin, Jinfeng Lai, Karl Mueller, Prabhakaran Munusamy, et al. 2013. “Surface Characterization of Nanomaterials and Nanoparticles: Important Needs and Challenging opportunities.” Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 31 (5): 50820. doi:10.1116/1.4818423.
   PubMed Web of Science ®Google Scholar
• Bai, W., Z. Zhang, W. Tian, X. He, Y. Ma, Y. Zhao, and Z. Chai. 2010. “Toxicity of Zinc Oxide Nanoparticles to Zebrafish Embryo: A Physicochemical Study of Toxicity Mechanism.” Journal of Nanoparticle Research 12 (5): 1645–1654. doi:10.1007/s11051-009-9740-9.
   Web of Science ®Google Scholar
• Bai, C., and M. Tang. 2020. “Toxicological Study of Metal and Metal Oxide Nanoparticles in Zebrafish.” Journal of Applied Toxicology: JAT 40 (1): 37–63. doi:10.1002/jat.3910.
   PubMed Web of Science ®Google Scholar
• Berg, J. M., A. Romoser, N. Banerjee, R. Zebda, and C. M. Sayes. 2009. “The Relationship between pH and Zeta Potential of ∼30 nm Metal Oxide Nanoparticle Suspensions Relevant to in Vitro Toxicological Evaluations.” Nanotoxicology 3 (4): 276–283. doi:10.3109/17435390903276941.
   Web of Science ®Google Scholar
• Borm, Paul J. A., David Robbins, Stephan Haubold, Thomas Kuhlbusch, Heinz Fissan, Ken Donaldson, Roel Schins, et al. 2006. “The Potential Risks of Nanomaterials: A Review Carried out for ECETOC.” Particle and Fibre Toxicology 3: 11. doi:10.1186/1743-8977-3-11.
   PubMed Web of Science ®Google Scholar
• Breiman, L. 1996. “Stacked Regressions.” Machine Learning 24 (1): 49–64. doi:10.1007/BF00117832.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Breiman, L., J. Friedman, R. Olshen, and C. Stone. 1984. Classification and Regression Trees. New York: Chapman and Hall.
   Google Scholar
• Bugel, S. M., R. L. Tanguay, and A. Planchart. 2014. “Zebrafish: A Marvel of High-Throughput Biology for 21st Century Toxicology.” Current Environmental Health Reports 1 (4): 341–352. doi:10.1007/s40572-014-0029-5.
   PubMedGoogle Scholar
• Cassano, A., R. L. Marchese Robinson, A. Palczewska, A. Puzyn, A. Gajewicz, L. Tran, S. Manganelli, and M. T. D. Cronin. 2016. “Comparing the CORAL and Random Forest Approaches for Modelling the in Vitro Cytotoxicity of Silica Nanomaterials.” Alternatives to Laboratory Animals: ATLA 44 (6): 533–556. doi:10.1177/026119291604400603.
   PubMed Web of Science ®Google Scholar
• Cawley, G. C., and N. L. C. Talbot. 2010. “On over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107.
   Web of Science ®Google Scholar
• Chen, T., and C. Guestrin. 2016. “Xgboost:  A  Scalable Tree Boosting System." arXiv:1603.02754. doi:10.1145/2939672.2939785
   Google Scholar
• Chen, T., T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, K. Chen, et al. 2019. xgboost: Extreme Gradient Boosting. R package version 0.90.0.2. https://CRAN.R-project.org/package=xgboost.
   Google Scholar
• Chen, S., Q. Zhang, Y. Hou, J. Zhang, and X.-J. Liang. 2013. “Nanomaterials in Medicine and Pharmaceuticals: Nanoscale Materials Developed with Less Toxicity and More Efficacy.” European Journal of Nanomedicine 5 (2): 61–79. doi:10.1515/ejnm-2013-0003.
   Google Scholar
• Comandella, Daniele, Stefania Gottardo, Iria Maria Rio-Echevarria, and Hubert Rauscher. 2020. “Quality of Physicochemical Data on Nanomaterials: An Assessment of Data Completeness and Variability.” Nanoscale 12 (7): 4695–4708. doi:10.1039/c9nr08323e.
   PubMed Web of Science ®Google Scholar
• Concu, R., V. V. Kleandrova, A. Speck-Planche, and M. N. D. S. Cordeiro. 2017. “Probing the Toxicity of Nanoparticles: A Unified in Silico Machine Learning Model Based on Perturbation Theory.” Nanotoxicology 11 (7): 891–906. doi:10.1080/17435390.2017.1379567.
   PubMed Web of Science ®Google Scholar
• Deane-Mayer, Z. A., and J. E. Knowles. 2016. “caretEnsemble: Ensembles of Caret Models.” R package version 2.0.0. https://CRAN.R-project.org/package=caretEnsemble
   Google Scholar
• Donaldson, K., F. A. Murphy, R. Duffin, and C. Poland. 2010. “Asbestos, Carbon Nanotubes and the Pleural Mesothelium: A Review of the Hypothesis regarding the Role of Long Fibre Retention in the Parietal Pleura, Inflammation and Mesothelioma.” Particle and Fibre Toxicology 7: 5. doi:10.1186/1743-8977-7-5.
   PubMed Web of Science ®Google Scholar
• Drucker, H., C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. “Support Vector Regression Machines.” Paper presented at the  Advances in Neural Information Processing Systems, Denver, CO, 155–161.
   Google Scholar
• Faraway, J. 2005. Linear Models with R. Boca Raton: Chapman & Hall/CRC.
   Google Scholar
• Farjadian, F.,. A. Ghasemi, O. Gohari, A. Roointan, M. Karimi, and M. R. Hamblin. 2019. “Nanopharmaceuticals and Nanomedicines Currently on the Market: challenges and Opportunities.” Nanomedicine (London, England) 14 (1): 93–126. doi:10.2217/nnm-2018-0120.
   PubMed Web of Science ®Google Scholar
• Firkowska, I., S. Giannona, J. Rojas-Chapana, K. Lücke, O. Brüstle, and M. Giersig. 2008. “Biocompatible Nanomaterials and Nanodevices Promising for Biomedical Applications.” In Nanomaterials for Application in Medicine and Biology, 1–15. Dordrecht: Springer. doi:10.1007/978-1-4020-6829-4_1.
   Google Scholar
• Fjodorova, N., M. Novic, A. Gajewicz, and B. Rasulev. 2017. “The Way to Cover Prediction for Cytotoxicity for All Existing Nano-sized Metal Oxides by Using Neural Network Method.” Nanotoxicology 11 (4): 475–483. doi:10.1080/17435390.2017.1310949.
   PubMed Web of Science ®Google Scholar
• Fleming, A., and W. K. Alderton. 2013. “Zebrafish in Pharmaceutical Industry Research: finding the Best Fit.” Drug Discovery Today: Disease Models 10 (1): e43–e50. doi:10.1016/j.ddmod.2012.02.006.
   Google Scholar
• Forest, V., J.-F. Hochepied, and J. Pourchez. 2019. “Importance of Choosing Relevant Biological End Points to Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment.” Chemical Research in Toxicology 32 (7): 1320–1326. doi:10.1021/acs.chemrestox.9b00022.
   PubMed Web of Science ®Google Scholar
• Gajewicz, A., T. D. M. Cronin, B. Rasulev, J. Leszczynski, and T. Puzyn. 2015. “Novel Approach for Efficient Predictions Properties of Large Pool of Nanomaterials Based on Limited Set of Species: Nano-Read-Across.” Nanotechnology 26 (1): 015701. doi:10.1088/0957-4484/26/1/015701.
   PubMed Web of Science ®Google Scholar
• Gousiadou, C., and R. L. Marchese Robinson. 2020. “Code for Gousiadou et al. Machine Learning Predictions of Concentration-Specific Aggregate Hazard Scores of Inorganic Nanomaterials in Embryonic Zebrafish (Version v2)." Zenodo Online Repository. doi:10.5281/zenodo.4284036.
   Google Scholar
• Guha, R. 2007. “Chemical Informatics Functionality in R.” Journal of Statistical Software 18: 1–16. doi:10.18637/jss.v018.i05.
   Web of Science ®Google Scholar
• Halamoda-Kenzaoui, B., M. Ceridono, P. Urbán, A. Bogni, J. Ponti, S. Gioria, and A. Kinsner-Ovaskainen. 2017. “The Agglomeration State of Nanoparticles Can Influence the Mechanism of Their Cellular Internalisation.” Journal of Nanbiotechnology 15 (1): 15–48. doi:10.1186/s12951-017-0281-6.
   PubMedGoogle Scholar
• Harper, B., D. Thomas, S. Chikkagoudar, N. Baker, K. Tang, A. Heredia-Langner, R. Lins, and S. Harper. 2015. “Comparative Hazard Analysis and Toxicological Modeling of Diverse Nanomaterials Using the Embryonic zebrafish (EZ) metric of toxicity.” Journal of Nanoparticle Research 17 (6): 250. doi:10.1007/s11051-015-3051-0.
   PubMed Web of Science ®Google Scholar
• Harper, S. L., J. L. Carriere, J. M. Miller, J. E. Hutchison, B. L. S. Maddux, and R. L. Tanguay. 2011. “Systematic Evaluation of Nanomaterial Toxicity: Utility of Standardized Materials and Rapid Assays.” ACS Nano 5 (6): 4688–4697. doi:10.1021/nn200546k.
   PubMed Web of Science ®Google Scholar
• Hawkins, D. M. 2004. “The Problem of Overfitting.” Journal of Chemical Information and Modeling 44 (1): 1–12. doi:10.1021/ci0342472.
   Google Scholar
• Hosu, O., M. Tertis, and C. Cristea. 2019. “Implication of Magnetic Nanoparticles in Cancer Detection.” Screening and Treatment. Magnetochemistry 5: 55. doi:10.3390/magnetochemistry5040055.
   Web of Science ®Google Scholar
• Hughes, M. P. 2000. “AC Electrokinetics: Applications for Nanotechnology.” Nanotechnology 11 (2): 124–132. doi:10.1088/0957-4484/11/2/314.
   Web of Science ®Google Scholar
• John, G. H., R. Kohavi, and K. Pfleger. 1994. “Irrelevant Features and the Subset Selection Problem.” In Machine Learning Proceedings, 121–129. Burlington, MA: Morgan Kauffman. doi:10.1016/B978-1-55860-335-6.50023-4.
   Google Scholar
• Karcher, Sandra C., Bryan J. Harper, Stacey L. Harper, Christine Ogilvie Hendren, Mark R. Wiesner, and Gregory V. Lowry. 2016. “Visualization Tool for Correlating Nanomaterial Properties and Biological Responses in Zebrafish.” Environmental Science: Nano 3 (6): 1280–1292. doi:10.1039/C6EN00273K.
   Web of Science ®Google Scholar
• Katz, L. M., K. Dewan, and R. L. Bronaugh. 2015. “Nanotechnology in Cosmetics.” Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association 85: 127–137. doi:10.1016/j.fct.2015.06.020.
   PubMed Web of Science ®Google Scholar
• Kim, S., P. A. Thiessen, E. E. Bolton, J. Chen, G. Fu, A. Gindulyte, L. Han, et al. 2016. “PubChem Substance and Compound Databases.” Nucleic Acids Research 44 (D1): D1202–D1213. doi:10.1093/nar/gkv951.
   PubMed Web of Science ®Google Scholar
• Kleandrova, V. V., F. Luana, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche, M. Natália, and D. S. Cordeiro. 2014. “Computational Ecotoxicology: Simultaneous Prediction of Ecotoxic Effects of Nanoparticles under Different Experimental Conditions.” Environment International 73: 288–294. doi:10.1016/j.envint.2014.08.009.
   PubMed Web of Science ®Google Scholar
• Klimeck, G., M. McLennan, S. B. Brophy, G. B. Adams, III, and M. S. Lundstrom. 2008. “nanoHUB.org: Advancing Education and Research in Nanotechnology.” Computing in Science & Engineering 10 (5): 17–23. Accessed 5 February 2020. https://nanohub.org/resources/23991/supportingdocs. doi:10.1109/MCSE.2008.120.
   Web of Science ®Google Scholar
• Kotzabasaki, M., I. Sotiropoulos, and H. Sarimveis. 2020. “QSAR Modeling of the Toxicity Classification of Superparamagnetic Iron Oxide Nanoparticles (SPIONs) in Stem-Cell Monitoring Applications: An Integrated Study from Data Curation to Model Development.” RSC Advances 10 (9): 5385–5391. doi:10.1039/C9RA09475J.
   PubMed Web of Science ®Google Scholar
• Kuhn, M. 2008. “Building Predictive Models in R Using the Caret Package.” Journal of Statistical Software 28: 1–26. doi:10.18637/jss.v028.i05.
   PubMed Web of Science ®Google Scholar
• Kuhn, M. 2019. “caret: Classification and Regression Training R package version 6.0-84.” http://topepo.github.io/caret/index.html.
   Google Scholar
• Kvålseth, O. T. 1985. “Cautionary Note about R 2.” The American Statistician 39 (4): 279–285. doi:10.1080/00031305.1985.10479448.
   Web of Science ®Google Scholar
• Labouta, H. I., N. Asgarian, K. Rinker, and D. T. Cramb. 2019. “Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature.” ACS Nano 13 (2): 1583–1594. doi:10.1021/acsnano.8b07562.
   PubMed Web of Science ®Google Scholar
• Lehner, R., X. Wang, S. Marsch, and P. Hunziker. 2013. “Intelligent Nanomaterials for Medicine: Carrier Platforms and Targeting Strategies in the Context of Clinical Application.” Nanomedicine: Nanotechnology, Biology, and Medicine 9 (6): 742–757. doi:10.1016/j.nano.2013.01.012.
   PubMed Web of Science ®Google Scholar
• Leisch, F., and E. Dimitriadou. 2010. “mlbench: Machine Learning Benchmark Problems.” R package version 2.1-1. http://rdrr.io/cran/mlbench
   Google Scholar
• Lewis, Richard W., Richard Billington, Eric Debryune, Armin Gamer, B. Lang, and Francis Carpanini. 2002. “Recognition of Adverse and Nonadverse Effects in Toxicity Studies.” Toxicologic Pathology 30 (1): 66–74. doi:10.1080/01926230252824725.
   PubMed Web of Science ®Google Scholar
• Liaw, A., and M. Wiener. 2002. “Classification and Regression by randomForest.” R News 2, 18–22.
   Google Scholar
• Lind, P., and T. Maltseva. 2003. “Support Vector Machines for the Estimation of Aqueous Solubility.” Journal of Chemical Information and Computer Sciences 43 (6): 1855–1859. doi:10.1021/ci034107s.
   PubMedGoogle Scholar
• Liu, X., K. Tang, S. Harper, B. Harper, J. A. Steevens, and R. Xu. 2013. “Predictive Modeling of Nanomaterial Exposure Effects in Biological Systems.” International Journal of Nanomedicine 8: 31–43. doi:10.2147/IJN.S40742.
   PubMed Web of Science ®Google Scholar
• Liu, X., K. Tang, S. Harper, B. Harper, J. Steevens, and R. Xu. “Predictive modeling of nanomaterial biological effects. In: Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference.” Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2012; Philadelphia, PA, USA. IEEE Xplore; 859–863. doi:10.1109/BIBMW.2012.6470254.
   Google Scholar
• Marchese Robinson, Richard L., Iseult Lynch, Willie Peijnenburg, John Rumble, Fred Klaessig, Clarissa Marquardt, Hubert Rauscher, et al. 2016. “How Should the Completeness and Quality of Curated Nanomaterial Data Be Evaluated?” Nanoscale 8 (19): 9919–9943. doi:10.1039/C5NR08944A.
   PubMed Web of Science ®Google Scholar
• Marchese Robinson, R. L., K. J. Roberts, and E. B. Martin. 2018. “The Influence of Solid State Information and Descriptor Selection on Statistical Models of Temperature Dependent Aqueous Solubility.” Journal of Cheminformatics 10 (1): 44. doi:10.1186/s13321-018-0298-3.
   PubMedGoogle Scholar
• Marchese Robinson, R. L., A. Palczewska, J. Palczewski, and N. Kidley. 2017. “ Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.” Journal of Chemical Information and Modeling 57 (8): 1773–1792. doi:10.1021/acs.jcim.6b00753.
   PubMed Web of Science ®Google Scholar
• Milborrow, S. 2019. “rpart.plot: Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'.” R package version 3.0.8. https://CRAN.R-project.org/package=rpart.plot
   Google Scholar
• Nanomaterial-Biological Interactions Knowledgebase (NBI). 2019. Accessed 10 September 2019. http://nbi.oregonstate.edu/.
   Google Scholar
• Natekin, A., and A. Knoll. 2013. “Gradient Boosting Machines, a Tutorial Front.” Frontiers in Neurorobotics 7: 21–21. doi:10.3389/fnbot.2013.00021.
   PubMed Web of Science ®Google Scholar
• Nelder, J., and R. Wedderburn. 1972. “Generalized Linear Models.” Journal of the Royal Statistical Society. Series A (General) 135 (3): 370–384. doi:10.2307/2344614.
   Web of Science ®Google Scholar
• Palanisamy, S., and Y.-M. Wang. 2019. “Superparamagnetic Iron Oxide Nanoparticulate System: synthesis, Targeting, Drug Delivery and Therapy in Cancer.” Dalton Transactions (Cambridge, England : 2003) 48 (26): 9490–9515. doi:10.1039/c9dt00459a.
   PubMed Web of Science ®Google Scholar
• Reis, Catarina Pinto, Ronald J. Neufeld, António J. Ribeiro, and Francisco Veiga. 2006. “Veiga, F. Nanoencapsulation I. Methods for Preparation of Drug-Loaded Polymeric Nanoparticles.” Nanomedicine: Nanotechnology, Biology, and Medicine 2 (1): 8–21. doi:10.1016/j.nano.2005.12.003.
   PubMed Web of Science ®Google Scholar
• Polishchuk, P. 2017. “Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future.” Journal of Chemical Information and Modeling 57 (11): 2618–2639. doi:10.1021/acs.jcim.7b00274.
   PubMed Web of Science ®Google Scholar
• Puzyn, T., N. Jeliazkova, H. Sarimveis, R. Marchese Robinson, V. Lobaskin, R. Rallo, A. N. Richarz, et al. 2018. “Perspectives from the NanoSafety Modelling Cluster on the Validation Criteria for (Q)SAR Models Used in Nanotechnology.” Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association 112: 478–494. doi:10.1016/j.fct.2017.09.037.
   PubMed Web of Science ®Google Scholar
• Puzyn, T., D. Leszczynska, and J. Leszczynski. 2009. “Toward the Development of "nano-QSARs": Advances and Challenges.” Small (Weinheim an Der Bergstrasse, Germany) 5 (22): 2494–2509. doi:10.1002/smll.200900179.
   PubMed Web of Science ®Google Scholar
• Qi, Y., T. Zhang, C. Jing, S. Liu, C. Zhang, P. J. J. Alvarez, and W. Chen. 2020. “Nanocrystal Facet Modulation to Enhance transferrin binding and cellular delivery.” Nature Communications 11 (1): 1262. doi:10.1038/s41467-020-14972-z.
   PubMed Web of Science ®Google Scholar
• R Core Team. 2018. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing. Vienna: Austria, http://www.R-project.org.
   Google Scholar
• Rai, Pratyush, Sechang Oh, Prashanth Shyamkumar, Mouli Ramasamy, Robert E. Harbaugh, and Vijay K. Varadan. 2014. “Textile Sensors with Mobile Wireless Platform for Wearable Health Monitoring of Neurological and Cardiovascular Disorders.” Journal of the Electrochemical Society 161 (2): B3116–B3150. doi:10.1149/2.012402jes.
   Web of Science ®Google Scholar
• Rogers, D., and M. Hahn. 2010. “Extended-Connectivity Fingerprints.” Journal of Chemical Information and Modeling 50 (5): 742–754. doi:10.1021/ci100050t.
   PubMed Web of Science ®Google Scholar
• Roy, P. P., S. Paul, I. Mitra, and K. Roy. 2009. “On Two Novel Parameters for Validation of Predictive QSAR Models.” Molecules (Basel, Switzerland) 14 (5): 1660–1701. doi:10.3390/molecules14051660.
   PubMed Web of Science ®Google Scholar
• Saini, B., and S. Srivastava. 2018. “Nanotoxicity Prediction Using Computational Modelling - Review and Future Directions.” IOP Conference Series: Materials Science and Engineering 348: 012005. doi:10.1088/1757-899X/348/1/012005.
   Google Scholar
• Salata, O. 2004. “Applications of Nanoparticles in Biology and Medicine.” Journal of Nanobiotechnology 2 (1): 3. doi:10.1186/1477-3155-2-3.
   PubMedGoogle Scholar
• Smola, Alex J., and Bernhard Schölkopf. 2004. “A Tutorial on Support Vector Regression.” Statistics and Computing 14 (3): 199–222. doi:10.1023/B:STCO.0000035301.49549.88.
   Web of Science ®Google Scholar
• Stewart, J. J. P. 2016. MOPAC2016, Stewart Computational Chemistry. Colorado Springs, CO: Stewart Computational Chemistry. http://OpenMOPAC.net.
   Google Scholar
• Stewart, J. J. 2013. “Optimization of Parameters for Semiempirical Methods VI: more Modifications to the NDDO Approximations and re-optimization of parameters.” Journal of Molecular Modeling 19 (1): 1–32. doi:10.1007/s00894-012-1667-x.
   PubMed Web of Science ®Google Scholar
• Strobl, C., A.-L. Boulesteix, A. Zeileis, and T. Hothorn. 2007. “Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution.” BMC Bioinformatics 8: 25. doi:10.1186/1471-2105-8-25.
   PubMed Web of Science ®Google Scholar
• Svetnik, Vladimir, Andy Liaw, Christopher Tong, J. Christopher Culberson, Robert P. Sheridan, and Bradley P. Feuston. 2003. “Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR modeling.” Journal of Chemical Information and Modeling 43 (6): 1947–1958. doi:10.1021/ci034160g.
   Google Scholar
• Svetnik, V., A. Liaw, C. Tong, and T. Wang. 2004. “Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules.” In Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, edited by F. Roli, J. Kittler, and T. Windeatt, Vol. 3077, 334–343. Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-25966-4_33.
   Google Scholar
• Therneau, T., and B. Atkinson. 2018. “rpart: Recursive Partitioning and Regression Trees.” R package version 4.1-13. https://CRAN.R-project.org/package=rpart
   Google Scholar
• Truong, L., S. L. Harper, and R. L. Tanguay. 2011. “Evaluation of Embryotoxicity Using the Zebrafish Model.” In: Drug Safety Evaluation. Methods in Molecular Biology (Methods and Protocols), edited by J. C. Gautier, Vol. 691. Dordrecht: Humana Press. ISBN 978-1-60327-186-8. doi:10.1007/978-1-60761-849-2_16.
   Google Scholar
• United States Environmental Protection Agency (EPA). 2019. “Administrator memo prioritizing efforts to reduce animal testing.” Accessed 5 February 2020. https://www.epa.gov/research/administrator-memo-prioritizing-efforts-reduce-animal-testing-september-10-2019
   Google Scholar
• Van der Laan, M. J., E. C. Polley, and A. E. Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1): 2194–6302. doi:10.2202/1544-6115.1309.
   Web of Science ®Google Scholar
• Vance, M., T. E; Kuiken, E. P. Vejerano, S. P. McGinnis, M. F. Hochella, Jr.; D. Rejeski, and M. S. Hull. 2015. “Nanotechnology in the Real World: Redeveloping the Nanomaterial Consumer Products Inventory.” Beilstein Journal of Nanotechnology 6: 1769–1780. doi:10.3762/bjnano.6.181.
   PubMed Web of Science ®Google Scholar
• Verschoor, A. J., S. Harper, C. J. E. Delmaar, M. V. D. Z. Park, A. J. A. M. Sips, M. G. Vijver, and W. J. G. M. Peijnenburg. 2019. “Systematic Selection of a Dose Metric for Metal-Based Nanoparticles.” NanoImpact 13: 70–75. doi:10.1016/j.impact.2019.01.002.
   Web of Science ®Google Scholar
• Wei, T., and V. Simko. 2017. “R Package "Corrplot": Visualization of a Correlation Matrix (Version 0.84).” Available from https://github.com/taiyun/corrplot.
   Google Scholar
• Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686. doi:10.21105/joss.01686.
   Google Scholar
• Wickham, H., R. François, L. Henry, and K. Müller. 2019. “dplyr: A Grammar of Data Manipulation.” R package version 0.8.3. https://CRAN.R-project.org/package=dplyr.
   Google Scholar
• Yan, X., A. Sedykh, W. Wang, X. Zhao, B. Yan, and H. Zhu. 2019. “In Silico Profiling Nanoparticles: Predictive Nanomodeling Using Universal Nanodescriptors and Various Machine Learning Approaches.” Nanoscale 11 (17): 8352–8362. doi:10.1039/c9nr00844f.
   PubMed Web of Science ®Google Scholar
• Zhou, Z., J. Son, B. Harper, Z. Zhou, and S. Harper. 2015. “Influence of Surface Chemical Properties on the Toxicity of Engineered Zinc Oxide Nanoparticles to Embryonic Zebrafish.” Beilstein Journal of Nanotechnology 6: 1568–1579. doi:10.3762/bjnano.6.160.
   PubMed Web of Science ®Google Scholar