Recursive feature elimination in random forest classification supports nanomaterial grouping-Reference-Cited by-同舟云学术

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Recursive feature elimination in random forest classification supports nanomaterial grouping

Published:2019-03 Issue: Volume:15 Page:100179
ISSN:2452-0748
Container-title:NanoImpact
language:en
Short-container-title:NanoImpact

Author:

Bahl Aileen,Hellack Bryan,Balas Mihaela,Dinischiotu Anca,Wiemann Martin,Brinkmann Joep,Luch Andreas,Renard Bernhard Y.,Haase Andrea

Funder

European Commission

BMBF

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality,Materials Science (miscellaneous)

Reference59 articles.

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3. Case studies putting the decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping) into practice;Arts;Regul. Toxicol. Pharmacol.,2016

4. Grouping of multi-walled carbon nanotubes to read-across genotoxicity: a case study to evaluate the applicability of regulatory guidance;Aschberger;Computational Toxicology,2019

5. Physicochemical characteristics of nanomaterials that affect pulmonary inflammation;Braakhuis;Part Fibre Toxicol,2014

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