论文标题
非小细胞肺癌的半参数综合相互作用分析
Semiparametric integrative interaction analysis for non-small-cell lung cancer
论文作者
论文摘要
在基因组分析中，识别与癌症结局或表型相关的标志物具有挑战性的挑战是很重要的。基于癌症的生物学机制和数据集的特征，本文提出了一种新型的综合相互作用方法，其中分别将遗传因素和环境因素作为参数和非参数组件包括在内。这种方法的目的是确定与癌症结果相关的遗传因素和基因 - 基因相互作用，同时估计环境因素的非线性影响。提出的方法基于阈值梯度定向正则化（TGDR）技术。仿真研究表明，所提出的方法在识别主要效应和相互作用方面的表现优于估计和预测准确性，与替代方法相比，该方法的估计和预测准确性有利。从癌症基因组地图集（TCGA）进行了非小细胞肺癌（NSCLC）数据集的分析，这表明所提出的方法可以鉴定具有重要意义的标记，并且在预测准确性，识别稳定性稳定性和计算成本方面具有良好的性能。
In the genomic analysis, it is significant while challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets as well, this paper proposes a novel integrative interaction approach under the semiparametric model, in which the genetic factors and environmental factors are included as the parametric and nonparametric components, respectively. The goal of this approach is to identify the genetic factors and gene-gene interactions associated with cancer outcomes, and meanwhile, estimate the nonlinear effects of environmental factors. The proposed approach is based on the threshold gradient directed regularization (TGDR) technique. Simulation studies indicate that the proposed approach outperforms in the identification of main effects and interactions, and has favorable estimation and prediction accuracy compared with the alternative methods. The analysis of non-small-cell lung carcinomas (NSCLC) datasets from The Cancer Genome Atlas (TCGA) are conducted, showing that the proposed approach can identify markers with important implications and have favorable performance in prediction accuracy, identification stability, and computation cost.
