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。
This paper focuses mainly on investigating and studying clustering analysis problems of high directional dimensional data , which includes gene expression data and text data .
本文针对高维数据的方向性及其聚类分析中出现的问题进行了研究。
There are many clustering algorithms have been applied to gene expression data now, and new algorithms are proposed continuously.
现在已有不少的算法开始应用于基因表达数据分析，而且不断有新的算法提出。
There are many clustering algorithms have been applied to gene expression data now, and new algorithms are proposed continuously.
现在已有不少的算法开始应用于基因表达数据分析，而且不断有新的算法提出。
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