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Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432486/ https://www.ncbi.nlm.nih.gov/pubmed/32722414 http://dx.doi.org/10.3390/ijms21155280 |
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author | Furxhi, Irini Murphy, Finbarr |
author_facet | Furxhi, Irini Murphy, Finbarr |
author_sort | Furxhi, Irini |
collection | PubMed |
description | The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models. |
format | Online Article Text |
id | pubmed-7432486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74324862020-08-24 Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning Furxhi, Irini Murphy, Finbarr Int J Mol Sci Article The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models. MDPI 2020-07-25 /pmc/articles/PMC7432486/ /pubmed/32722414 http://dx.doi.org/10.3390/ijms21155280 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Furxhi, Irini Murphy, Finbarr Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title | Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title_full | Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title_fullStr | Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title_full_unstemmed | Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title_short | Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning |
title_sort | predicting in vitro neurotoxicity induced by nanoparticles using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432486/ https://www.ncbi.nlm.nih.gov/pubmed/32722414 http://dx.doi.org/10.3390/ijms21155280 |
work_keys_str_mv | AT furxhiirini predictinginvitroneurotoxicityinducedbynanoparticlesusingmachinelearning AT murphyfinbarr predictinginvitroneurotoxicityinducedbynanoparticlesusingmachinelearning |