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Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells
Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryo...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937330/ https://www.ncbi.nlm.nih.gov/pubmed/27207879 http://dx.doi.org/10.1093/nar/gkw450 |
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author | Yamane, Junko Aburatani, Sachiyo Imanishi, Satoshi Akanuma, Hiromi Nagano, Reiko Kato, Tsuyoshi Sone, Hideko Ohsako, Seiichiroh Fujibuchi, Wataru |
author_facet | Yamane, Junko Aburatani, Sachiyo Imanishi, Satoshi Akanuma, Hiromi Nagano, Reiko Kato, Tsuyoshi Sone, Hideko Ohsako, Seiichiroh Fujibuchi, Wataru |
author_sort | Yamane, Junko |
collection | PubMed |
description | Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development. |
format | Online Article Text |
id | pubmed-4937330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-49373302016-07-11 Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells Yamane, Junko Aburatani, Sachiyo Imanishi, Satoshi Akanuma, Hiromi Nagano, Reiko Kato, Tsuyoshi Sone, Hideko Ohsako, Seiichiroh Fujibuchi, Wataru Nucleic Acids Res Computational Biology Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development. Oxford University Press 2016-07-08 2016-05-20 /pmc/articles/PMC4937330/ /pubmed/27207879 http://dx.doi.org/10.1093/nar/gkw450 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Yamane, Junko Aburatani, Sachiyo Imanishi, Satoshi Akanuma, Hiromi Nagano, Reiko Kato, Tsuyoshi Sone, Hideko Ohsako, Seiichiroh Fujibuchi, Wataru Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title | Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title_full | Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title_fullStr | Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title_full_unstemmed | Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title_short | Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
title_sort | prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937330/ https://www.ncbi.nlm.nih.gov/pubmed/27207879 http://dx.doi.org/10.1093/nar/gkw450 |
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