Weighted Area Under the Receiver Operating Characteristic Curve and Its Application to Gene Selection.
Weighted Area Under the Receiver Operating Characteristic Curve and Its Application to Gene Selection.
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DOI：
10.1111/j.1467-9876.2010.00713.x
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发表时间：
2010-08
期刊：
影响因子：
--
通讯作者：
Fine JP
中科院分区：
文献类型：
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作者：
Li J;Fine JP
ROC 曲线下部分面积 (PAUC) 已被提议用于基因选择，并随后应用于实际数据分析。实证研究发现，该衡量标准存在几个关键弱点，例如无法反映不同决策阈值的非均匀权重，从而导致大量的联系。我们在本文中提出了 ROC 曲线下加权面积（WAUC）来解决与 PAUC 相关的问题。我们提出的方法在描述基因的辨别准确性方面具有更大的灵活性。介绍了非参数和参数估计方法，包括作为特例的 PAUC，以及估计器的理论特性。我们还提供了一个简单的方差公式，产生了一种用于 PAUC 非参数估计的新颖方差估计器，这在之前的工作中已被证明具有挑战性。所提出的方法允许敏感性分析，从而可以评估不同权重函数对基因排名的影响，并且可以跨权重合成结果。对两个著名的微阵列数据集的模拟和重新分析说明了 WAUC 的实用性。
Partial area under the ROC curve (PAUC) has been proposed for gene selection in and thereafter applied in real data analysis. It was noticed from empirical studies that this measure has several key weaknesses, such as an inability to reflect nonuniform weighting of different decision thresholds, resulting in large numbers of ties. We propose the weighted area under the ROC curve (WAUC) in this paper to address the problems associated with PAUC. Our proposed measure enjoys a greater flexibility to describe the discrimination accuracy of genes. Nonparametric and parametric estimation methods are introduced, including PAUC as a special case, along with theoretical properties of the estimators. We also provide a simple variance formula, yielding a novel variance estimator for nonparametric estimation of PAUC, which has proven challenging in previous work. The proposed methods permit sensitivity analyses, whereby the impact of differing weight functions on gene rankings may be assessed and results may be synthesized across weights. Simulations and re-analysis of two well-known microarray datasets illustrate the practical utility of WAUC.