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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >ADDITIVE RISK ANALYSIS OF MICROARRAY GENE EXPRESSION DATA VIA CORRELATION PRINCIPAL COMPONENT REGRESSION
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ADDITIVE RISK ANALYSIS OF MICROARRAY GENE EXPRESSION DATA VIA CORRELATION PRINCIPAL COMPONENT REGRESSION

机译：通过相关主成分回归分析微阵列基因表达数据的附加风险分析

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摘要

In order to predict future patients' survival time based on their microarray gene expression data, one interesting question is how to relate genes to survival outcomes. In this paper, by applying a semi-parametric additive risk model in survival analysis, we propose a new approach to conduct a careful analysis of gene expression data with the focus on the model's predictive ability. In the proposed method, we apply the correlation principal component regression to deal with right censoring survival data under the semi-parametric additive risk model frame with high-dimensional covariates. We also employ the time-dependent area under the receiver operating characteristic curve and root mean squared error for prediction to assess how well the model can predict the survival time. Furthermore, the proposed method is able to identify significant genes, which are significantly related to the disease. Finally, the proposed useful approach is illustrated by the diffuse large B-cell lymphoma data set and breast cancer data set. The results show that the model fits the data sets very well.
机译：为了根据他们的微阵列基因表达数据预测未来患者的生存时间，一个有趣的问题是如何将基因与生存结果联系起来。在本文中，通过在生存分析中应用半参数累加风险模型，我们提出了一种新的方法来进行基因表达数据的仔细分析，重点是模型的预测能力。在提出的方法中，我们使用相关主成分回归来处理带有高协变量的半参数加性风险模型框架下的右删失生存数据。我们还采用了接收器工作特性曲线和均方根误差下随时间变化的区域进行预测，以评估模型预测生存时间的能力。此外，所提出的方法能够鉴定与疾病显着相关的重要基因。最后，弥散性大B细胞淋巴瘤数据集和乳腺癌数据集说明了所提出的有用方法。结果表明，该模型非常适合数据集。

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