Analysis of these large datasets requires sophisticated algorithms and bioinformatics to identify individual markers of interest or to derive signatures or patterns of many markers (reviewed by Cristoni and Bernardi, 2004; Englbrecht and Facius, 2005; Tinker et al., 2006). Although these methods are continually evolving and being improved, there is still a great need for novel approaches to data analysis, especially with regard to network oriented models that can incorporate many different types of data to fully integrate the vast complexity of biology in health and disease. However, identifying biomarker patterns or specific changes in genes or the products of gene expression in tumors is only the beginning of the process to develop cancer biomarkers.

Before a candidate biomarker can be put into use, it must undergo several stages of confirmation, validation, and qualification for use (Wagner, 2002; Feng et al., 2004; Ransohoff, 2004, 2005; Simon, 2005; De Bortoli and Biglia, 2006). Analytical validation is the process of assessing the assay or measurement performance characteristics, while qualification is the evidentiary process of linking a biomarker with the biology and clinical end-