This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object descriptions including output definitions, input parameter descriptions, and applicable limits.

Funding for this research was provided by the National Science Foundation (NSF) and the Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093, Center for the Environmental Implications of NanoTechnology (CEINT), the Carnegie Institute of Technology (CIT) Dean's Fellowship, the Prem Narain Srivastava Legacy Fellowship, the Neil and Jo Bushnell Fellowship, the Bertucci Fellowship in Engineering, and the Steinbrenner Institute for Environmental Education and Research (SEER). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or the EPA. This work has not been subjected to EPA review and no official endorsement should be inferred.

Gernand J. and Casman E. "Selecting Nanoparticle Properties to Mitigate Risks to Workers and the Public – A Machine Learning Modeling Framework to Compare Pulmonary Toxicity Risks of Nanomaterials." Proc. of IMECE2013. No. 62687.

Researchers should cite this work as follows:

• Jeremy M Gernand (2013), "Random Forest Model Objects for Pulmonary Toxicity Risk Assessment," https://nanohub.org/resources/17539.

  BibTex | EndNote

1. carbon nanotubes
2. health and safety
3. machine learning
4. risk assessment and management
5. risk modeling
6. Titanium Dioxide
7. toxicity