Machine learning for nanomaterial toxicity risk assessment

Jeremy M. Gernand, Elizabeth A. Casman

Research output: Contribution to journalArticle

7 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence

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