CCPortal
DOI10.1021/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
ISSN1549-9596
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
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Martin, Todd M.]的文章
[Grulke, Christopher M.]的文章
[Young, Douglas M.]的文章
百度学术
百度学术中相似的文章
[Martin, Todd M.]的文章
[Grulke, Christopher M.]的文章
[Young, Douglas M.]的文章
必应学术
必应学术中相似的文章
[Martin, Todd M.]的文章
[Grulke, Christopher M.]的文章
[Young, Douglas M.]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。