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DOI | 10.1371/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 |
ISSN | 1932-6203 |
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
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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/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). |
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