Gan Bin, Zheng Chun-Hou, Zhang Jun, Wang Hong-Qiang
College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China.
College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China ; College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
Biomed Res Int. 2014;2014:420856. doi: 10.1155/2014/420856. Epub 2014 Feb 11.
Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data. Then we use sparse representation classifier (SRC) to build tumor classification model. The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO.
准确的肿瘤分类对于癌症的恰当治疗至关重要。到目前为止，稀疏表示（SR）在肿瘤分类方面已展现出其卓越性能。本文构想了一种基于稀疏表示的新方法，通过使用基因表达数据进行肿瘤分类。在所提出的方法中，我们首先使用潜在低秩表示来提取显著特征并从原始样本数据中去除噪声。然后我们使用稀疏表示分类器（SRC）来构建肿瘤分类模型。在几个真实世界数据集上的实验结果表明，我们的方法比包括支持向量机（SVM）、稀疏表示分类器（SRC）和套索（LASSO）在内的先前分类方法更高效且更有效。
