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Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniqu...

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Autores principales: Choi, Jang-Sik, Ha, My Kieu, Trinh, Tung Xuan, Yoon, Tae Hyun, Byun, Hyung-Gi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904177/
https://www.ncbi.nlm.nih.gov/pubmed/29666463
http://dx.doi.org/10.1038/s41598-018-24483-z
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author Choi, Jang-Sik
Ha, My Kieu
Trinh, Tung Xuan
Yoon, Tae Hyun
Byun, Hyung-Gi
author_facet Choi, Jang-Sik
Ha, My Kieu
Trinh, Tung Xuan
Yoon, Tae Hyun
Byun, Hyung-Gi
author_sort Choi, Jang-Sik
collection PubMed
description A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
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spelling pubmed-59041772018-04-30 Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources Choi, Jang-Sik Ha, My Kieu Trinh, Tung Xuan Yoon, Tae Hyun Byun, Hyung-Gi Sci Rep Article A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model. Nature Publishing Group UK 2018-04-17 /pmc/articles/PMC5904177/ /pubmed/29666463 http://dx.doi.org/10.1038/s41598-018-24483-z Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Choi, Jang-Sik
Ha, My Kieu
Trinh, Tung Xuan
Yoon, Tae Hyun
Byun, Hyung-Gi
Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title_full Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title_fullStr Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title_full_unstemmed Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title_short Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
title_sort towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904177/
https://www.ncbi.nlm.nih.gov/pubmed/29666463
http://dx.doi.org/10.1038/s41598-018-24483-z
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