Relevant gene selection is crucial for analyzing cancer gene expression datasets including two types of tumors in cancer classification. Intrinsic interactions among selected genes cannot be fully identified by most existing gene selection methods. In this paper, we propose a weighted general group lasso (WGGL) model to select cancer genes in groups. A gene grouping heuristic method is presented based on weighted gene co-expression network analysis. To determine the importance of genes and groups, a method for calculating gene and group weights is presented in terms of joint mutual information. To implement the complex calculation process of WGGL, a gene selection algorithm is developed. Experimental results on both random and three cancer gene expression datasets demonstrate that the proposed model achieves better classification performance than two existing state-of-the-art gene selection methods.

中文翻译：

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用于癌症分类中基因选择的加权通用组套索


相关基因选择对于分析癌症基因表达数据集（包括癌症分类中的两种肿瘤）至关重要。大多数现有的基因选择方法无法完全识别所选基因之间的内在相互作用。在本文中，我们提出了一种加权通用组套索（WGGL）模型来按组选择癌症基因。提出了一种基于加权基因共表达网络分析的基因分组启发式方法。为了确定基因和群体的重要性，提出了一种根据联合互信息计算基因和群体权重的方法。为了实现WGGL的复杂计算过程，开发了基因选择算法。随机和三个癌症基因表达数据集的实验结果表明，所提出的模型比两种现有的最先进的基因选择方法实现了更好的分类性能。