Gene cluster algorithm based on most similarity tree
Gene cluster algorithm based on most similarity tree
复制标题
DOI：
10.1109/hpcasia.2005.41
复制
发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
Lu Xin-guo;Lin Ya-ping;Liang Xiao-long;Yi Ye-qing;Cai Li-jun;Wang Hai-jun
中科院分区：
文献类型：
--
作者：
Lu Xin-guo;Lin Ya-ping;Liang Xiao-long;Yi Ye-qing;Cai Li-jun;Wang Hai-jun
随着DNA阵列技术的发展，产生了大规模的DNA阵列表达数据集。构建功能基因组并表示未知基因的功能非常重要。该手稿描述了一种基于最相似树（CMST）的基因聚类方法，它是具有相似性度量的等价关系的等价组的划分。引入相似性测度的Gap统计来确定最优的相似性测度，并提出一种基于CMST的最优自适应基因聚类算法（OS-CMST）。 CMST聚类方法能够得到全局最优聚类，实验结果表明CMST优于K-means和SOM等传统聚类方法
As the development of DNA array technology, large-scale DNA array expression data sets are produced. It is very important to construct the functional genome and denote the functions of unknown genes. This manuscript describes a gene cluster method based on the most similarity tree (CMST), which is a partition of equivalence groups of equivalence relation with similarity measure. The Gap statistic of similarity measure is introduced to determine the most optimal similarity measure and an optimally self-adaptive gene cluster algorithm based on CMST (OS-CMST) is proposed. The cluster method of CMST can get the global optimal clusters and the experiment results show that CMST outperform traditional cluster methods of K-means and SOM