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Issue 95, 2015
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Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models

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Abstract

Knowledge about the toxicity of nanomaterials and factors responsible for such phenomena are important tasks necessary for efficient human health protection and safety risk estimation associated with nanotechnology. In this study, the causation inference method within structure-activity relationship modeling for nanomaterials was introduced to elucidate the underlying structure of the nanotoxicity data. As case studies, the structure-activity relationships for toxicity of metal oxide nanoparticles (nano-SARs) towards BEAS-2B and RAW 264.7 cell lines were established. To describe the nanoparticles, the simple ionic, fragmental and “liquid drop model” based descriptors that represent the nanoparticles' structure and characteristics were applied. The developed classification nano-SAR models were validated to confirm reliability of predicting toxicity for all studied metal oxide nanoparticles. Developed models suggest different mechanisms of nanotoxicity for the two types of cells.

Graphical abstract: Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models

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Publication details

The article was received on 15 Jun 2015, accepted on 07 Sep 2015 and first published on 08 Sep 2015


Article type: Paper
DOI: 10.1039/C5RA11399G
Author version available: Download Author version (PDF)
Citation: RSC Adv., 2015,5, 77739-77745
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    Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models

    N. Sizochenko, B. Rasulev, A. Gajewicz, E. Mokshyna, V. E. Kuz'min, J. Leszczynski and T. Puzyn, RSC Adv., 2015, 5, 77739
    DOI: 10.1039/C5RA11399G

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