主成分分析
审查(临床试验)
比例危险模型
主成分回归
数据挖掘
计算机科学
微阵列分析技术
回归分析
回归
相关性
基因芯片分析
数据集
统计
人工智能
微阵列
机器学习
数学
生物
基因
基因表达
遗传学
几何学
作者
Qiang Zhao,Jianguo Sun
标识
DOI:10.2202/1544-6115.1153
摘要
Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.
科研通智能强力驱动
Strongly Powered by AbleSci AI