Kernel hierarchical gene clustering from microarray expression data

Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying g...

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Veröffentlicht in:	Bioinformatics 2003-11, Vol.19 (16), p.2097-2104
Hauptverfasser:	Qin, Jie, Lewis, Darrin P., Noble, William Stafford
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Animals Artificial Intelligence Biological and medical sciences Cell Cycle - genetics Cluster Analysis DNA microarrays Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Mice Neoplasms - classification Neoplasms - genetics Oligonucleotide Array Sequence Analysis - methods Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Sequence Analysis, DNA - methods Yeasts - genetics
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