According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
根据基因表达数据的特点,提出一种高精度的基于密度的聚类算法DENGENE。
We used random matrix theory (RMT) to remove the noises in lung cancer gene expression data and used the modules approach and the hierarchical clustering approach to construct the gene networks.
利用随机矩阵理论(RMT)方法除去肺癌基因表达数据中的噪声,并将去噪后的数据分别用模块方法和等级聚类方法进行处理。
This paper proposed a new non-parametric algorithm for clustering gene expression data.
提出了一种用于基因表达数据的无参数聚类算法。
应用推荐