Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results
Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results
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DOI：
10.1021/jm049970d
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发表时间：
2004-08-26
影响因子：
7.3
通讯作者：
Davies, JW
中科院分区：
文献类型：
--
作者：
Klon, AE;Glick, M;Davies, JW
我们之前已经证明，机器学习技术可以提高高通量对接（HTD）结果的丰富性。然而，在先前研究的情况下，朴素贝叶斯分类器的应用未能改善HTD单独无法产生可接受的富集的情况下的富集。我们在这里提出了一个协议，以挽救不良的对接结果先验使用的组合排名中位数的共识评分和朴素贝叶斯分类。
We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.