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Random Forest Model Objects for Pulmonary Toxicity Risk Assessment

By Jeremy M Gernand

Carnegie Mellon University

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Abstract

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.

Sponsored by

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.

Publications

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.

Cite this work

Researchers should cite this work as follows:

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

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nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.