Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, 999078, China.
Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, 999078, China.
Comput Biol Med. 2018 Sep 1;100:203-208. doi: 10.1016/j.compbiomed.2018.07.009. Epub 2018 Jul 17.
Methods based on a L penalty have been utilized to solve the variable selection problem associated with the Cox proportional hazards model. One limitation of the existing methods for survival analysis is that these ignore the regulatory networks and pathways information. To merge prior pathway information into the analysis of genomic data, we proposed a network-based regularization method for the L penalty and applied it to high-dimensional survival analysis data. This method used a L regularized solver and network that penalizes a Cox proportional hazards model with respect to the sparsity of the regression and the smoothness between the coefficients in a given network. Based on the limited simulation studies and real breast cancer gene expression datasets, the experimental results showed that our method achieves a higher predictive accuracy than previous methods. Even though fewer genes were selected compared to those using previous methods, results showed stronger associations with cancer. The results of the analysis were also validated using GeneCards.
基于 L 惩罚的方法已被用于解决与 Cox 比例风险模型相关的变量选择问题。现有的生存分析方法的一个局限性是，这些方法忽略了调控网络和途径信息。为了将先验途径信息合并到基因组数据分析中，我们提出了一种基于网络的 L 惩罚正则化方法，并将其应用于高维生存分析数据。该方法使用 L 正则化求解器和网络，根据回归的稀疏性和给定网络中系数之间的平滑性对 Cox 比例风险模型进行惩罚。基于有限的模拟研究和真实的乳腺癌基因表达数据集，实验结果表明，我们的方法比以前的方法具有更高的预测准确性。即使与使用以前的方法相比，选择的基因更少，但结果显示与癌症的相关性更强。还使用 GeneCards 对分析结果进行了验证。
