Data integration and genomic medicine

Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, USA. <>
Journal of Biomedical Informatics (Impact Factor: 2.19). 03/2007; 40(1):5-16. DOI: 10.1016/j.jbi.2006.02.007
Source: PubMed


Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

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    • "One of the daunting tasks in bioinformatics is managing the vast amount of genomic data generated from large scale experiments (Barrett et al., 2007; Kann, 2009). The challenges enormous biological data presents are in different levels of variations and complexities (Louie et al., 2007); this constitutes some of the challenges of our generation. The problems range from difficulty associated with understanding the human genome, the detailed functions of gene encoding proteins, and sourcing for useful information for drug design among others. "
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