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Coupled two-way clustering analysis of data

机译:数据的双向双向聚类分析

摘要

A novel coupled two-way clustering approach to gene microarray data analysis, for identifying subsets of the genes and samples, such that when one of these items is used to cluster the other, stable and significant partitions emerge. The method of the present invention preferably uses iterative clustering in order to execute this search in an efficient way. This approach is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method of the present invention was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on these subsets, partitions and correlations were found that were masked and hidden when the full data set was used in the analysis.
机译:一种新颖的双向双向聚类方法,用于基因微阵列数据分析,用于鉴定基因和样品的子集,这样,当其中一项被用来聚类时,就会出现稳定而重要的分区。本发明的方法优选地使用迭代聚类,以便以有效的方式执行该搜索。这种方法尤其适用于基因微阵列数据,在该数据中,各种生物学机制对基因表达水平的贡献都被大量的实验数据纠缠在一起。本发明的方法被应用于结肠癌和白血病的两个基因微阵列数据集。通过识别数据的相关子集并关注这些子集,可以发现在分析中使用完整数据集时被掩盖和隐藏的分区和相关性。

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