Machine learning for nanomaterial toxicity risk assessment

Jeremy Michael Gernand, Elizabeth A. Casman

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Many questions about the mechanisms of nanomaterial toxicity are unanswered and an applicable general theory of nanomaterial toxicity doesn't seem to be on the horizon. To help with this problem, the authors use machine learning algorithms with quantitative analytical capabilities in a meta-analysis of carbon nanotube pulmonary toxicity studies. Such analyses can identify the material varieties most likely to be the riskiest and guide future development towards those most likely to pose the least risk.

Original languageEnglish (US)
Article number6871719
Pages (from-to)84-88
Number of pages5
JournalIEEE Intelligent Systems
Volume29
Issue number3
DOIs
StatePublished - Jan 1 2014

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Nanostructured materials
Risk assessment
Toxicity
Learning systems
Learning algorithms
Carbon nanotubes

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

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Machine learning for nanomaterial toxicity risk assessment. / Gernand, Jeremy Michael; Casman, Elizabeth A.

In: IEEE Intelligent Systems, Vol. 29, No. 3, 6871719, 01.01.2014, p. 84-88.

Research output: Contribution to journalArticle

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