Classification tree models for the prediction of blood-brain barrier passage of drugs
Classification tree models for the prediction of blood-brain barrier passage of drugs
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DOI：
10.1021/ci050518s
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发表时间：
2006-05-01
影响因子：
5.6
通讯作者：
Vander Heyden, Yvan
中科院分区：
文献类型：
--
作者：
Deconinck, Eric;Zhang, Menghui H.;Vander Heyden, Yvan
使用分类树建模和预测分子通过血脑屏障的途径进行了评估。这些模型是用从文献中提取的147个分子的数据集建立和评估的。第一步，构建单个分类树并评估其预测能力。在第二步中，尝试使用一组150个分类树的增强方法来提高预测能力。采用了离散自适应增强算法和真实自适应增强算法，并进行了比较。对所使用的数据集获得了高预测的分类树，并且模型可以通过boosting进行改进。在本研究的背景下，离散自适应增强的效果略好于真实自适应增强。
The use of classification trees for modeling and predicting the passage of molecules through the blood-brain barrier was evaluated. The models were built and evaluated using a data set of 147 molecules extracted from the literature. In the first step, single classification trees were built and evaluated for their predictive abilities. In the second step, attempts were made to improve the predictive abilities using a set of 150 classification trees in a boosting approach. Two boosting algorithms, discrete and real adaptive boosting, were used and compared. High-predictive classification trees were obtained for the data set used, and the models could be improved with boosting. In the context of this research, discrete adaptive boosting gives slightly better results than real adaptive boosting.