Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean to 0.830 and the AUC to 0.935. Computer simulations indicate that the previous method is susceptible to sample size imbalance, whereas the proposed method is more robust while achieving almost identical classification accuracy. Furthermore, the results confirm that threshold adjustment is an effective countermeasure to sample size imbalance.

中文翻译：

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用于计数数据判别问题的贝叶斯方法，并应用于多基因座短串联重复数据集。

短串联重复序列（STR）是一种DNA多态性。这项研究考虑了判别分析，以使用STR数据库确定测试个体的数量，该数据库包含在多个位点观察到的STR的长度。讨论了基于贝叶斯因子的判别方法，并提出了一种改进的方法。主要问题是开发一种对样本大小不平衡相对鲁棒的方法，确定选择基因座的程序以及处理先验分布中的参数。先前的研究对g均值（两个总体的分类精度的几何平均值）的分类精度为0.748，对AUC（接收器工作特性曲线下的面积）的分类精度为0.867。我们将g均值的最大值提高到0.830，将AUC的最大值提高到0.935。计算机仿真表明，先前的方法容易受到样本大小不平衡的影响，而所提出的方法则在达到几乎相同的分类精度的同时更加鲁棒。此外，结果证实阈值调整是解决样本量不平衡的有效措施。