title: Analysis of large-scale gene expression data.
abstract: 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.
authors.affiliation: Department of Genetics, Stanford University Medical Center, Stanford, 94306-5120, USA. sherlock@genome.stanford.edu
authors.forename: G
authors.initials: G
authors.lastname: Sherlock
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing / Q000379:methods; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis