期刊
PHARMACOGENOMICS
卷 8, 期 5, 页码 473-482出版社
FUTURE MEDICINE LTD
DOI: 10.2217/14622416.8.5.473
关键词
class comparison; class prediction; cross validation; gene class testing; gene selection; multiple selection criteria; multiple testing; significance analysis
One major challenge with the use of microarray technology is the analysis of massive amounts of gene-expression data for various applications. This review addresses the key aspects of the microarray gene-expression data analysis for the two most common objectives: class comparison and class prediction. Class comparison mainly aims to select which genes are differentially expressed across experimental conditions. Gene selection is separated into two steps: gene ranking and assigning a significance level. Class prediction uses expression profiling analysis to develop a prediction model for patient selection, diagnostic prediction or prognostic classification. Development of a prediction model involves two components: model building and performance assessment. It also describes two additional data analysis methods: gene-class testing and multiple ordering criteria.
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