Analysis of large-scale gene expression data

被引：276
|
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
收藏
页码：201 / 205
页数：5
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