CCPortal
DOI10.1371/journal.pone.0099863
Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology
Zhang, Jun1,2; Hsieh, Jui-Hua3; Zhu, Hao1,2
发表日期2014-06-20
ISSN1932-6203
卷号9期号:6
英文摘要

In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.


语种英语
WOS记录号WOS:000338276300021
来源期刊PLOS ONE
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/60198
作者单位1.Rutgers State Univ, Dept Chem, Camden, NJ 08102 USA;
2.Rutgers Ctr Computat & Integrat Biol, Camden, NJ USA;
3.Natl Inst Environm Hlth Sci, Div Natl Toxicol Program, Biomol Screening Branch, Res Triangle Pk, NC USA
推荐引用方式
GB/T 7714
Zhang, Jun,Hsieh, Jui-Hua,Zhu, Hao. Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology[J]. 美国环保署,2014,9(6).
APA Zhang, Jun,Hsieh, Jui-Hua,&Zhu, Hao.(2014).Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology.PLOS ONE,9(6).
MLA Zhang, Jun,et al."Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology".PLOS ONE 9.6(2014).
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
[Zhang, Jun]的文章
[Hsieh, Jui-Hua]的文章
[Zhu, Hao]的文章
百度学术
百度学术中相似的文章
[Zhang, Jun]的文章
[Hsieh, Jui-Hua]的文章
[Zhu, Hao]的文章
必应学术
必应学术中相似的文章
[Zhang, Jun]的文章
[Hsieh, Jui-Hua]的文章
[Zhu, Hao]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。