Grouped Gene Selection of Cancer via Adaptive Sparse Group Lasso Based on Conditional Mutual Information
Grouped Gene Selection of Cancer via Adaptive Sparse Group Lasso Based on Conditional Mutual Information
复制标题
基于条件互信息的自适应稀疏组套索对癌症进行分组基因选择
DOI：
10.1109/tcbb.2017.2761871
复制
发表时间：
2018-11-01
影响因子：
4.5
通讯作者：
Meng, Deyuan
中科院分区：
文献类型：
--
作者：
Li, Juntao;Dong, Wenpeng;Meng, Deyuan
本文讨论了肿瘤分类和分组基因选择问题。利用癌症微阵列数据上的加权基因共表达网络来识别与生物通路相对应的模块，并在此基础上提出了基因分组策略。利用每个分组内的条件互信息，提出了一个综合准则，并构造了数据驱动的权重。它们显示出评估个体基因的重要性和对每组中所有其他成对基因改善相关性的影响的能力。在此基础上，提出了一种自适应稀疏群算法，并据此提出了一种改进的块下降算法。在4个癌症数据集上的实验结果表明，所提出的自适应稀疏群套索可以有效地进行分类和分组基因选择。
This paper deals with the problems of cancer classification and grouped gene selection. The weighted gene co-expression network on cancer microarray data is employed to identify modules corresponding to biological pathways, based on which a strategy of dividing genes into groups is presented. Using the conditional mutual information within each divided group, an integrated criterion is proposed and the data-driven weights are constructed. They are shown with the ability to evaluate both the individual gene significance and the influence to improve correlation of all the other pairwise genes in each group. Furthermore, an adaptive sparse group lasso is proposed, by which an improved blockwise descent algorithm is developed. The results on four cancer data sets demonstrate that the proposed adaptive sparse group lasso can effectively perform classification and grouped gene selection.