A gene pathway is defined as a set of genes that functionally work together to regulate a certain biological process. Gene pathway expression data, which is a special case of highly correlated high‐dimensional data, exhibits the ‘small n and large p’ problem. Pathway analysis can take into account the dependency structures among genes and the possibility that several moderately regulated genes may have significant impacts on the clinical outcomes. To test the significance of gene pathways in the presence of subgroups, we propose a finite mixture model of semiparametric Bayesian survival kernel machine regressions (fm‐BKSurv). Within each hidden group, we model the unknown function of gene pathways via a Gaussian kernel machine. We demonstrate how fm‐BKSurv excels in terms of true positive rate, false positive rate, accuracy, and precision in a simulation study, and further illustrate the outperformance of fm‐BKSurv in detecting significant gene pathways using a gene pathway expression dataset of breast cancer patients.

中文翻译：

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半参数贝叶斯生存核机器回归的有限混合：在乳腺癌基因途径亚组分析中的应用

基因途径被定义为一组基因，这些基因在功能上协同工作以调节特定的生物过程。基因途径表达数据是高度相关的高维数据的特例，表现出“小n和大p”。' 问题。途径分析可以考虑基因之间的依赖性结构，以及几个受适当调节的基因可能对临床结果产生重大影响的可能性。为了测试存在亚组时基因途径的重要性，我们提出了半参数贝叶斯生存核机器回归（fm-BKSurv）的有限混合模型。在每个隐藏的组中，我们通过高斯核机对基因途径的未知功能进行建模。我们在模拟研究中证明了fm-BKSurv在真实阳性率，假阳性率，准确性和精确度方面的卓越表现，并进一步说明了fm-BKSurv在使用乳腺癌的基因途径表达数据集检测重要的基因途径方面表现出色耐心。