Department of Electronic Engineering, Xiamen University, Fujian 361005, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
Department of Electronic Engineering, Xiamen University, Fujian 361005, China.
Comput Biol Chem. 2017 Apr;67:92-101. doi: 10.1016/j.compbiolchem.2016.12.010. Epub 2016 Dec 31.
Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new L1-regularized feature selection method is defined to remove irrelevant and redundant features and to select the important features (biomarkers). In the next stage, PLS-based feature extraction is implemented on the selected features to extract synthesis features that best reflect discriminating characteristics for classification. The suitability of the proposal is demonstrated in an empirical study done with ten widely used microarray datasets, and the results show its effectiveness and competitiveness compared with four state-of-the-art methods. The experimental results on St Jude dataset shows that our method can be effectively applied to microarray data analysis for subtype prediction and the discovery of gene coexpression.
降维是机器学习和数据挖掘中的一项关键技术，广泛应用于医学、生物信息学和遗传学等领域。在本文中，我们提出了一种两阶段的局部降维方法，用于微阵列数据的分类。在第一阶段，定义了一种新的 L1 正则化特征选择方法，以去除不相关和冗余的特征，并选择重要的特征（生物标志物）。在下一阶段，在选择的特征上实现基于 PLS 的特征提取，以提取最佳反映分类区分特征的综合特征。该提案在对十个广泛使用的微阵列数据集进行的实证研究中得到了验证，结果表明与四种最先进的方法相比，它具有有效性和竞争力。在 St Jude 数据集上的实验结果表明，我们的方法可以有效地应用于微阵列数据分析，用于亚类预测和基因共表达的发现。
