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Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine by Francisco Azuaje

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Guest commentary on chapter 7: Commentary on ‘disease biomarkers and biological interaction networks’

Zhongming Zhao

Departments of Biomedical Informatics, Psychiatry and Cancer Biology Vanderbilt University School of Medicine, Nashville, TN 37203, USA

Discovery of novel molecular biomarkers of disease has recently become a major research topic in translational research thanks to the coming of the genomic era. Because of its biological and economical importance, biomarker discovery has been strongly supported by US government agencies as well as funding agencies in other countries. In the blueprint for the genomic era proposed by Francis Collins and colleagues in the US National Human Genome Research Institute (NHGRI) in 2003, most of the grand challenges in the applications of genomics to health are much related to biomarker discovery, including identification of disease causal markers at the gene, locus, network and pathway levels, prediction of disease susceptibility and drug response, early detection of illness, and development of powerful new therapeutic approaches to disease (Collins et al., 2003). As described in Chapter 7, network-based approaches using different types of ‘omic’ data or their combination have been extensively applied to the biomarker discovery in many diseases such as cancers, cardiovascular diseases, and diabetes. The ‘omic’ data that have been recruited in biomarker discovery includes protein-protein interactions, protein-gene interactions, gene expression ...

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