在大规模基因表达谱的数据分析中引入了一种全新的基于贝叶斯模型的聚类算法。
A novel clustering algorithm based on Bayesian model was introduced into the analysis of large-scale gene expression profiles.
第一步是聚类基因表达数据。
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。
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.
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。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
常用于基因表达数据分析的聚类方法有很多。
There are lots of cluster methods applied to the analysis of 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 proposed a new non-parametric algorithm for clustering gene expression data.
将该种模型运用于公开的白血病基因表达数据集进行实验，实验表明该方法能自动获取基因表达数据的聚类数，并得到较高的分类准确率。
We applied the model to analyze the expression data set of leukaemia. The experimental result proved that this model can get cluster Numbers automatically and a high accuracy of classification.
〉基因表达数据的并行双向聚类算法。
A systematic comparison and evaluation of biclustering methods for gene expression data.
〉基因表达数据的并行双向聚类算法。
A systematic comparison and evaluation of biclustering methods for gene expression data.
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