Tumor Clustering based on Penalized Matrix Decomposition

摘要：

A reliable and precise identification of the type of tumors is essential for effective treatment of cancer. In this paper, we proposed a novel method to cluster tumors using gene expression data. In this method, we use penalized matrix decomposition (PMD) to extract metasamples from gene expression data. Specially, a metasample can capture structures inherent in the samples in one class. In addition, we present how to use the factors of PMD to cluster the samples. Compared with traditional methods, such as HC, SOM and NMF etc., our method can find the samples with complex classes. At the same time, the number of clusters can be determined automatically.

作者: Chun-Hou Zheng Juan Wang To-Yee Ng Chi Keung Shiu

作者单位: College of Information and Communication Technology,Qufu Normal University,Rizhao, Shandong, China Department of Computing,The Hong Kong Polytechnic University,Hong Kong, China Department of Computing, The Hong Kong Polytechnic University,Hong Kong, China

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）