A Discriminative Feature Extraction Approach for Tumor Classification Using Gene Expression Data
A Discriminative Feature Extraction Approach for Tumor Classification Using Gene Expression Data
复制标题
使用基因表达数据进行肿瘤分类的判别性特征提取方法
DOI：
10.2174/1574893611666160728114747
复制
发表时间：
2016-01-01
影响因子：
4
通讯作者：
Liang, Cheng
中科院分区：
文献类型：
--
作者：
Mei, Qinglin;Zhang, Huaxiang;Liang, Cheng
背景：肿瘤分类是基因表达数据的重要应用之一。由于基因芯片数据的高维性，降维在基于基因表达谱的肿瘤分类中起着至关重要的作用。目的：本研究的主要目的是通过特征提取方法对基因表达数据进行降维，以提高肿瘤分类的准确率。方法：提出了一种新的用于肿瘤分类的有监督特征提取方法--判别混合结构保持投影。该方法利用混合表示法来有效地刻画基因表达数据的结构，同时考虑了邻域表示和稀疏表示。具体地说，我们的算法通过同时最小化类内距离和最大化类间距离来增强降维后数据的可分性。结果：在5个公开可用的肿瘤数据集上的实验结果表明，该方法与一些最新的特征提取和特征选择方法相比是有效的。结论：该算法能够增强投影后数据的可分性，从而提高基因表达数据的肿瘤分类精度。
Background: Tumor classification is one of the most important applications of gene expression data. Due to high dimensionality in microarray data, dimensionality reduction plays a crucial role in tumor classification based on gene expression profiles.Objective: The primary objective of this study is to increase the accuracy of tumor classification by reducing the dimensionality of gene expression data with feature extraction methods.Method: In this paper, we propose a novel supervised feature extraction method for tumor classification called discriminant hybrid structure preserving projections. The proposed method utilizes hybrid representation to efficiently characterize the structure of gene expression data, where both neighbor representation and sparse representation are taken into account. Specifically, our algorithm enhances the data separability after dimensionality reduction by simultaneously minimizing the within-class distance and maximizing the between-class distance. Moreover, it employs an imbalanced adjustment factor during the extraction process to overcome the class imbalance problem in tumor datasets.Results: Experiments on five publicly available tumor datasets demonstrate the effectiveness of the proposed method in comparison with a number of state-of-the-art feature extraction and feature selection methods.Conclusion: The proposed algorithm can enhance the separability of data after projections and thus improve the tumor classification accuracy of gene expression data.