Abstract
The advent of Omics technologies as genomics and proteomics has brought the hope of discovering novel biomarkers that can be used to diagnose, predict, and monitor the progress of disease. The importance of data mining to identify biological markers for the diagnostic classification and prognostic assessment in the context of microarray and proteomic data has been increasingly recognized. We present an overview of general data mining methods and their applications to biomarker discovery with particular focus on genomics and proteomics data. Two case studies are exemplarily presented, and relevant data mining terminology and techniques are explained.
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Acknowledgments
This work was supported in part by a grant from the National Cancer Institute (U24CA126480-01), part of NCI’s Clinical Proteomic Technologies Initiative (http://proteomics.cancer.gov), awarded to Dr. Fred Regnier (PI) and Dr. Jake Chen (co-PI). We thank Hui Huang and Jiao Li for providing a case study.
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Zhang, F., Chen, J.Y. (2011). Data Mining Methods in Omics-Based Biomarker Discovery. In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_24
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DOI: https://doi.org/10.1007/978-1-61779-027-0_24
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