ABSTRACT
With proteomic and other high-throughput genomic profiling technologies, such as microarrays that allow for the simultaneous analysis of expression levels of thousands or even tens of thousands of biomarkers, we are able to expand our ability to characterize and understand disease processes at the molecular level and the heterogeneity surrounding them. As advances in biotechnology continue to support this remarkable expansion, however, the need for extracting and synthesizing information from the volumes of expression data has created an equally challenging research area in the development of corresponding statistical methods for their analysis. Within the context of biomedical and clinical research, a common objective of proteomic data analysis is the selection of biomarkers, from among the thousands
profiled, that characterize groups of similar phenotypes based on a small number of samples.
