Managing bias in ROC curves
Managing bias in ROC curves
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DOI：
10.1007/s10822-008-9181-z
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发表时间：
2008-03-01
影响因子：
3.5
通讯作者：
Webster-Clark, Daniel J.
中科院分区：
文献类型：
--
作者：
Clark, Robert D.;Webster-Clark, Daniel J.
两个修改的标准使用的受试者工作特性（ROC）曲线评估虚拟筛选方法提出。第一种是用半对数图（pROC图）代替通常使用的线性图，包括在进行“曲线下面积”（AUC）计算时。这样做是一种简单的方法，可以使统计数据偏向于识别恢复曲线中早期而不是后期的“命中”。第二个建议的修改需要根据其所属的导联系列的大小对每个活动进行加权。两个加权方案进行了描述：算术，其中每个活跃的权重是成反比的集群的大小，它来自和谐波，其中权重是成反比的等级内的每个活跃的类。两种方案都能够区分有偏和无偏的筛选统计量，但调和加权AUC特别强调将每类活性物质的代表置于恢复曲线早期的能力。
Two modifications to the standard use of receiver operating characteristic (ROC) curves for evaluating virtual screening methods are proposed. The first is to replace the linear plots usually used with semi-logarithmic ones (pROC plots), including when doing "area under the curve" (AUC) calculations. Doing so is a simple way to bias the statistic to favor identification of "hits" early in the recovery curve rather than late. A second suggested modification entails weighting each active based on the size of the lead series to which it belongs. Two weighting schemes are described: arithmetic, in which the weight for each active is inversely proportional to the size of the cluster from which it comes; and harmonic, in which weights are inversely proportional to the rank of each active within its class. Either scheme is able to distinguish biased from unbiased screening statistics, but the harmonically weighted AUC in particular emphasizes the ability to place representatives of each class of active early in the recovery curve.