Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction
Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction
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DOI：
10.1021/ci0601315
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发表时间：
2006-09-25
影响因子：
5.6
通讯作者：
Diller, David J.
中科院分区：
文献类型：
--
作者：
Klon, Anthony E.;Lowrie, Jeffrey F.;Diller, David J.
我们已经实现了一个朴素贝叶斯分类器，它使用高斯分布对连续数值数据进行建模。在吸收，分布，代谢和排泄预测领域的几个案例表明，这种方法是上级的朴素贝叶斯分类器，其中连续的化学描述符建模为二进制数据的实施。我们证明，这种增强的性能，与其他实现相比，是独立的描述符集的选择。我们还比较了三个实现的朴素贝叶斯分类器与其他先前描述的模型的性能。
We have implemented a naive Bayesian classifier which models continuous numerical data using a Gaussian distribution. Several cases of interest in the area of absorption, distribution, metabolism, and excretion prediction are presented which demonstrate that this approach is superior to the implementation of naive Bayesian classifiers in which continuous chemical descriptors are modeled as binary data. We demonstrate that this enhanced performance, upon comparison with other implementations, is independent of the descriptor sets chosen. We also compare the performance of three implementations of naive Bayesian classifiers with other previously described models.