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首页> 外文期刊>Journal of Clinical Bioinformatics >Tools to identify linear combination of prognostic factors which maximizes area under receiver operator curve
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Tools to identify linear combination of prognostic factors which maximizes area under receiver operator curve

机译：识别预后因素线性组合的工具，该组合可最大化接收者操作员曲线下方的面积

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Background The linear combination of variables is an attractive method in many medical analyses targeting a score to classify patients. In the case of ROC curves the most popular problem is to identify the linear combination which maximizes area under curve (AUC). This problem is complete closed when normality assumptions are met. With no assumption of normality search algorithm are avoided because it is accepted that we have to evaluate AUC nd times where n is the number of distinct observation and d is the number of variables. Methods For d?=?2, using particularities of AUC formula, we described an algorithm which lowered the number of evaluations of AUC from n2 to n(n-1)?+?1. For d?>?2 our proposed solution is an approximate method by considering equidistant points on the unit sphere in Rd where we evaluate AUC. Results The algorithms were applied to data from our lab to predict response of treatment by a set of molecular markers in cervical cancers patients. In order to evaluate the strength of our algorithms a simulation was added. Conclusions In the case of no normality presented algorithms are feasible. For many variables computation time could be increased but acceptable.
机译：背景技术在许多医学分析中，变量的线性组合是一种有吸引力的方法，其目标是评分以对患者进行分类。对于ROC曲线，最流行的问题是确定使曲线下面积（AUC）最大化的线性组合。当满足正态性假设时，此问题已完全解决。在不假设正态性的情况下，避免使用搜索算法，因为我们接受了必须对AUC次进行评估，其中n是不同观测值的数量，d是变量的数量。方法对于d？=？2，利用AUC公式的特殊性，我们描述了一种将AUC的评估次数从n2减少到n（n-1）？+？1的算法。对于d？>？2，我们提出的解决方案是一种近似方法，它考虑了我们评估AUC的Rd的单位球面上的等距点。结果该算法被应用于我们实验室的数据，以通过一系列分子标记物预测宫颈癌患者的治疗反应。为了评估我们算法的强度，添加了仿真。结论在没有正态性的情况下，提出的算法是可行的。对于许多变量，计算时间可以增加，但可以接受。

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