Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Computational System Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.
Genes (Basel). 2019 Mar 7;10(3):200. doi: 10.3390/genes10030200.
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.
探索乳腺癌亚型的内在差异非常重要。这些内在差异与临床诊断和治疗方案的指定密切相关。随着生物和医学数据集的积累，有许多不同的组学数据可以从不同的角度进行观察。结合这些多种组学数据可以提高预测的准确性。同时，也有许多不同的数据库可供我们下载不同类型的组学数据。在本文中，我们使用雌激素受体 (ER)、孕激素受体 (PR)、人表皮生长因子受体 2 (HER2) 来定义乳腺癌亚型，并使用 SMO-MKL 算法对任意两种乳腺癌亚型进行分类。我们从 TCGA 收集了 mRNA 数据、甲基化数据和拷贝数变异 (CNV) 数据来对乳腺癌亚型进行分类。多核学习 (MKL) 用于区分这些组学数据。使用多核的三种组学数据的结果优于使用多核的单一组学数据的结果。此外，还对特征选择过程中发现的显著基因和途径进行了分析。在实验中，所提出的方法优于其他最先进的方法，并且具有丰富的生物学解释。
