论文标题
GEE-TGDR：一种纵向特征选择算法及其在接受免疫疗法治疗的牛皮癣患者的LNCRNA表达谱的应用
GEE-TGDR: A longitudinal feature selection algorithm and its application to lncRNA expression profiles for psoriasis patients treated with immune therapies
论文作者
论文摘要
随着高通量技术的快速发展，纵向基因表达实验在生物医学领域变得负担得起，并且越来越普遍。广义估计方程（GEE）方法是一种广泛使用的统计方法，用于分析纵向数据。在纵向法学数据分析中，特征选择必须进行。在各种现有的特征选择方法中，嵌入式方法，即阈值梯度下降正则化（TGDR）由于其出色的特性而脱颖而出。 GEE与TGDR的一致性是一个有前途的领域，目的是确定可以解释跨时间的动态变化的相关标记。在这项研究中，我们提出了一种新的新型特征选择算法，以实现纵向结果：Gee-TGDR。在GEE-TGDR方法中，GEE模型的相应准类函数是要优化的目标函数，并且通过TGDR方法完成了优化和特征选择。我们应用了GEE-TGDR方法纵向LNCRNA基因表达数据集，该数据集研究了牛皮癣患者对免疫治疗的治疗反应。在不同的工作相关结构下，鉴定出包括10个相关LNCRNA在内的列表，其预测精度为80％和有意义的生物学解释。总而言之，预计所提出的GEE-TGDR方法在OMICS数据分析中广泛应用。
With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method, namely, threshold gradient descent regularization (TGDR) stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. In this study, we proposed a new novel feature selection algorithm for longitudinal outcomes:GEE-TGDR. In the GEE-TGDR method, the corresponding quasi-likelihood function of a GEE model is the objective function to be optimized and the optimization and feature selection are accomplished by the TGDR method. We applied the GEE-TGDR method a longitudinal lncRNA gene expression dataset that examined the treatment response of psoriasis patients to immune therapy. Under different working correlation structures, a list including 10 relevant lncRNAs were identified with a predictive accuracy of 80 % and meaningful biological interpretation. To conclude, a widespread application of the proposed GEE-TGDR method in omics data analysis is anticipated.
