Helma C, Kramer S
Machine Learning Lab, University Freiburg, D-79110 Freiburg, Germany.
Bioinformatics. 2003 Jul 1;19(10):1179-82. doi: 10.1093/bioinformatics/btg084.
The Predictive Toxicology Challenge (PTC) was initiated to stimulate the development of advanced techniques for predictive toxicology models. The goal of this challenge was to compare different approaches for the prediction of rodent carcinogenicity, based on the experimental results of the US National Toxicology Program (NTP).
111 sets of predictions for 185 compounds have been evaluated on quantitative and qualitative scales to select the most predictive models and those with the highest toxicological relevance. The accuracy of the submitted predictions was between 25 and 79 %. An evaluation of the most accurate models by toxicological experts showed, that it is still hard for domain experts to interpret the submitted models and to put them into relation with toxicological knowledge.
PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.
预测毒理学挑战（PTC）旨在推动预测毒理学模型先进技术的发展。该挑战的目标是根据美国国家毒理学计划（NTP）的实验结果，比较预测啮齿动物致癌性的不同方法。
已从定量和定性尺度对185种化合物的111组预测进行了评估，以选择最具预测性的模型和毒理学相关性最高的模型。提交的预测准确率在25%至79%之间。毒理学专家对最准确模型的评估表明，领域专家仍难以解释提交的模型并将其与毒理学知识联系起来。
PTC的详细信息和数据可在以下网址找到：http://www.predictive-toxicology.org/ptc/ 。
