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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Hybrid L 1/2??+?2 method for gene selection in the Cox proportional hazards model
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Hybrid L 1/2??+?2 method for gene selection in the Cox proportional hazards model

机译：Hybrid L 1/2 ?? +？2 Cox比例危险模型中基因选择方法

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Background and objectiveAn important issue in genomic research is to identify the significant genes that related to survival from tens of thousands of genes. Although Cox proportional hazards model is a conventional survival analysis method, it does not induce the gene selection. MethodsIn this paper, we extend the hybrid L1/2??+?2regularization (HLR) idea to the censored survival situation, a new edition of sparse Cox model based on the HLR method has been proposed. We develop two algorithms for solving the HLR penalized Cox model; one is the coordinate descent algorithm with HLR thresholding operator, the other is the weight iteration method. ResultsThe proposed method was tested on six public mRNA data sets of serval kinds of cancers, AML, Breast cancer, Pancreatic cancer, DLBCL and Melanoma. The test results indicate that the method identified a small subset of genes but essential while giving best or equivalent predictive performance, as compared to some popular methods. ConclusionsThe results of empirical and simulations imply that the proposed strategy is highly competitive in?studying high dimensional survival data among several state-of-the-art methods.
机译：基因组研究中的背景和目标重要问题是鉴定与成千上万基因的生存相关的重要基因。虽然Cox比例危害模型是一种常规的存活分析方法，但它不会诱导基因选择。方法本文提出了一种缩短截取的生存情况，延伸了​​混合动力L1 / 2 ?? +？2分钟（HLR）概念，提出了一种基于HLR方法的新版稀疏COX模型。我们开发了解决HLR惩罚COX模型的两个算法;一个是具有HLR阈值运算符的坐标血换算法，另一个是重量迭代方法。结果在六种公共mRNA数据集中测试了六种公共mRNA数据集，AML，乳腺癌，胰腺癌，DLBCL和黑色素瘤。测试结果表明，与一些流行的方法相比，该方法鉴定了一小部分基因，而是必不可少的，同时提供最佳或等同的预测性能。结论实证和仿真的结果意味着所提出的策略在竞争力方面是竞争力的竞争力，研究了几种最先进的方法中的高维生存数据。

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