Analysis of large-scale gene expression data

被引：277

作者：

Sherlock, G ^{[1
]}

机构：

[1] Stanford Univ, Med Ctr, Dept Genet, Stanford, CA 94306 USA

来源：

CURRENT OPINION IN IMMUNOLOGY | 2000年 / 12卷 / 02期

关键词：

D O I：

10.1016/S0952-7915(99)00074-6

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

100102 ;

摘要：

The advent of cDNA and oligonucleotide microarray technologies has led to a paradigm shift in biological investigation, such that the bottleneck in research is shifting from data generation to data analysis. Hierarchical clustering, divisive clustering, self-organizing maps and k-means clustering have all been recently used to make sense of this mass of data.

引用

页码：201 / 205

页数：5

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