An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
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ILP 辅助毒理学模型和 PTE-3 实验的评估
DOI：
10.1007/3-540-48751-4_27
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
D. Bristol
中科院分区：
文献类型：
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作者：
A. Srinivasan;R. King;D. Bristol
预测毒理学评估（或 PTE）挑战赛为机器学习技术提供了与毒理学预测专业技术竞争的机会。使用 ILP 项目结果的毒性模型在本次挑战下提出的 PTE-2 实验中表现出色。我们在这里报告对此类模型的评估，包括：（1）定量性能，与专家合作开发的模型进行比较； (2)毒理学的潜在解释价值。结果似乎表明：(a) 在类别分布和错误成本范围内，一些使用 ILP 辅助构建的显式模型似乎比大多数专家辅助的模型更接近最优。鉴于测试数据的缺乏，对此需要谨慎解释； (b) 命题和 ILP 技术的结合使用似乎产生了包含结构和生物学特征的不寻常组合的模型； (c) 需要付出巨大努力来解释输出结果，这强烈表明需要投入更大的努力将输出结果转化为“毒理学家友好”的形式。根据从这些结果中吸取的教训，我们提出了一种新的预测毒理学评估实验——PTE-3——它将解决之前研究的一些重要缺陷。
The Predictive Toxicology Evaluation (or PTE) Challenge provided Machine Learning techniques with the opportunity to compete against specialised techniques for toxicology prediction. Toxicity models that used findings from ILP programs have performed creditably in the PTE-2 experiment proposed under this challenge. We report here on an assessment of such models along scales of: (1) quantitative performance, in comparison to models developed with expert collaboration; and (2) potential explanatory value for toxicology. Results appear to suggest the following: (a) across of range of class distributions and error costs, some explicit models constructed with ILP-assistance appear closer to optimal than most expert-assisted ones. Given the paucity of test-data, this is to be interpreted cautiously; (b) a combined use of propositional and ILP techniques appears to yield models that contain unusual combinations of structural and biological features; and (c) significant effort was required to interpret the output, strongly indicating the need to invest greater effort in transforming the output into a “toxicologist-friendly” form. Based on the lessons learnt from these results, we propose a new predictive toxicology evaluation experiment — PTE-3 — which will address some important shortcomings of the previous study.