The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

中文翻译：

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聚类与稀疏学习相结合的癌症双层基因选择

基于基因表达谱数据的癌症诊断已引起生物医学领域的广泛关注。这类数据通常具有高维、噪声等特点。本文提出了一种基于聚类和稀疏学习的混合基因选择方法，以高精度选择关键基因。我们首先提出了一种过滤方法，该方法结合了k均值聚类算法和信噪比排序方法，然后将加权基因共表达网络应用于简化的数据集以识别与生物途径相对应的模块。此外，我们通过使用组桥和稀疏组套索作为包装方法来选择关键基因。最后，我们对六个癌症数据集进行了一些数值实验。数值结果表明我们提出的方法在基因选择和癌症分类方面取得了良好的性能。