College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China.
IEEE Trans Nanobioscience. 2011 Jun;10(2):86-93. doi: 10.1109/TNB.2011.2144998. Epub 2011 Jul 7.
This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using nonnegative matrix factorization (NMF) or sparse NMF (SNMF), and then we extract features from the selected genes by virtue of NMF or SNMF. At last, we apply support vector machines (SVM) to classify the tumor samples using the extracted features. In order for a better classification, a modified SNMF algorithm is also proposed. The experimental results on benchmark three microarray data sets validate that the proposed method is efficient. Moreover, the biological meaning of the selected genes are also analyzed.
本文提出了一种使用基因表达数据进行肿瘤分类的新方法。在提出的方法中，我们首先使用非负矩阵分解（NMF）或稀疏 NMF（SNMF）选择基因，然后利用 NMF 或 SNMF 从选择的基因中提取特征。最后，我们使用提取的特征应用支持向量机（SVM）对肿瘤样本进行分类。为了更好的分类，还提出了一种改进的 SNMF 算法。在三个基准微阵列数据集上的实验结果验证了该方法的有效性。此外，还分析了选择基因的生物学意义。
