Bahler D, Bristol D W
Dept. of Computer Science, North Carolina State University, Raleigh 27695-8206, USA.
Proc Int Conf Intell Syst Mol Biol. 1993;1:29-37.
This paper presents results from an ongoing effort in applying a variety of induction-based methods to the problem of predicting the biological activity of noncongeneric (structurally dissimilar) chemicals. It describes initial experiments, the long-term goal of which is to assist toxicologists, cancer researchers, regulators, and others to predict the toxic effects of chemical compounds. We describe a series of experiments in tree and rule induction from a set of example chemicals whose carcinogenicity has been determined from long-term animal studies, and compare the resulting classification accuracy with eight published human and computer predictions for a common set of 44 test chemicals. The accuracy of our system is comparable to the most accurate human expert prediction yet published, and exceeds that of any of the computer-based predictions in the literature. The induced rules provide confirmation of current expert heuristic knowledge in this domain. These early results show that an inductive approach has excellent potential in predictive toxicology.
本文介绍了一项正在进行的工作成果,即应用多种基于归纳的方法来解决预测非同类(结构不同)化学物质生物活性的问题。它描述了初步实验,其长期目标是帮助毒理学家、癌症研究人员、监管机构及其他人员预测化合物的毒性作用。我们描述了一系列从一组示例化学物质中进行树归纳和规则归纳的实验,这些化学物质的致癌性已通过长期动物研究确定,并将所得分类准确率与针对一组44种测试化学物质的八篇已发表的人类和计算机预测结果进行比较。我们系统的准确率与迄今发表的最准确的人类专家预测相当,且超过了文献中任何基于计算机的预测。归纳出的规则证实了该领域当前专家的启发式知识。这些早期结果表明,归纳方法在预测毒理学中具有巨大潜力。