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Analysis of High Dimensional Gene Data Combining Correlation Principal Component Regression and Additive Risk Model

机译:相关主成分回归和添加性风险模型的高尺寸基因数据分析

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One problem of interest is to relate genes to survival outcomes of patients for the purpose of building regression models to predict future patients' survival based on their gene expression data. Applying semiparametric additive risk model of survival analysis, we propose a new approach to conduct the analysis of gene expression data with the focus on model's predictive ability. The method modifies the correlation principal component regression to handle the censoring problem of survival data. In addition, we employ the time dependent AUC and RMSEP to assess how well the model predicts the survival time. Furthermore, the proposed method is able to identify significant genes that are related to the disease. Finally, this proposed approach is illustrated by the diffuse large B-cell lymphoma (DLBCL) data set. The results show that the model fits the data set very well.
机译:兴趣的一个问题是将基因与患者的生存结果相关,以便建立回归模型以基于其基因表达数据来预测未来患者的存活。应用Semiparametric添加剂风险模型的存活分析,我们提出了一种新方法来进行基因表达数据的分析,重点是模型的预测能力。该方法修改相关主成分回归以处理存活数据的审查问题。此外,我们采用时间依赖的AUC和RMSEP来评估模型如何预测生存时间。此外,所提出的方法能够鉴定与疾病有关的重要基因。最后,通过弥漫性大B细胞淋巴瘤(DLBCL)数据集来说明该方法。结果表明,该模型非常适合数据设置。

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