Recent PLoS One article explains “Profiling Animal Toxicants by Automatically Mining Public Bioassay Data”

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Recent PLoS One article explains “Profiling Animal Toxicants by Automatically Mining Public Bioassay Data”

The growth in publicly available toxicity data holds great potential for toxicologists hoping to construct predictive computational models that increasingly eliminate the need for animal tests. As more in vitro bioassay and chemical structure data come “online,” computational models stand to become more accurate and informative. But there are pitfalls, as well: uniform data standards and ontologies are still evolving, and there is considerable variation in the quality, consistency, and completeness of open access data. As a result, the data informing computational models must still be, somewhat subjectively, manually curated by experts.

In Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology, authors Jun Zhang, Jui-Hua Hsieh, and Hao Zhu describe an automated data mining approach that assembles response profiles for potential animal toxicants using data publically available in PubChem.

From the abstract:

…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 potential 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.

Full citation: Zhang J, Hsieh J-H, Zhu H (2014). Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology. PLoS ONE 9(6): e99863. (OPEN access)

(Posted on September 11, 2014)