CHAPTER 9: Bioinformatics and Statistics: Computational Discovery, Verification, and Validation of Functional Biomarkers
Published:10 Jun 2013
F. Zhang and R. Drabier, in Comprehensive Biomarker Discovery and Validation for Clinical Application, ed. P. Horvatovich and R. Bischoff, The Royal Society of Chemistry, 2013, pp. 243-268.
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The advent of omics technologies such as genomics and proteomics has the hope of discovering novel biomarkers that can be used to diagnose, predict, and monitor the progress of disease. The enormous amount of data generated by high‐throughput proteomics, metabolomics, and genomics technologies requires sophisticated statistical techniques to differentiate between disease individuals and healthy individuals and identify candidate biomarkers. Many novel methods have been developed and applied for the purpose of identifying and using biomarkers to improve disease understanding, and to tailor medication use in individual patients with the goals of enhancing efficacy and minimizing toxicity. High‐dimensional investigations where thousands of genotypes, transcripts, methylation markers, and metabolites are measured together with environmental risk factors and clinical information are considered powerful tools to achieve these goals and are, therefore, an important focus of current research in clinical application.