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DOI | 10.1021/ci400267h |
Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models | |
Martin, Todd M.1; Grulke, Christopher M.2; Young, Douglas M.1; Russom, Christine L.3; Wang, Nina Y.4; Jackson, Crystal R.5; Barron, Mace G.5 | |
发表日期 | 2013-09-01 |
ISSN | 1549-9596 |
卷号 | 53期号:9页码:2229-2239 |
英文摘要 | The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms. |
语种 | 英语 |
WOS记录号 | WOS:000330097200004 |
来源期刊 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
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来源机构 | 美国环保署 |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/56456 |
作者单位 | 1.US EPA, Natl Risk Management Res Lab, Cincinnati, OH 45268 USA; 2.US EPA, Natl Exposure Res Lab, Res Triangle Pk, NC 27711 USA; 3.US EPA, Natl Hlth & Environm Effects Res Lab, Duluth, MN 55804 USA; 4.US EPA, Natl Ctr Environm Assessment, Cincinnati, OH 45268 USA; 5.US EPA, Natl Hlth & Environm Effects Res Lab, Gulf Breeze, FL 32561 USA |
推荐引用方式 GB/T 7714 | Martin, Todd M.,Grulke, Christopher M.,Young, Douglas M.,et al. Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models[J]. 美国环保署,2013,53(9):2229-2239. |
APA | Martin, Todd M..,Grulke, Christopher M..,Young, Douglas M..,Russom, Christine L..,Wang, Nina Y..,...&Barron, Mace G..(2013).Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models.JOURNAL OF CHEMICAL INFORMATION AND MODELING,53(9),2229-2239. |
MLA | Martin, Todd M.,et al."Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models".JOURNAL OF CHEMICAL INFORMATION AND MODELING 53.9(2013):2229-2239. |
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