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Yearb Med Inform 2007; 16(01): 98-105
DOI: 10.1055/s-0038-1638533
Survey
Georg Thieme Verlag KG Stuttgart

Section 7: Bioinformatics: Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine

L. J. Frey
1   Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
,
V. Maojo
2   Biomedical Informatics Group, Universidad Politecnica de Madrid, Spain
,
J. A. Mitchell
1   Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
› Author AffiliationsThe authors thank George Komatsoulis for discussions on domain modeling and interoperability for caBIG™, as well as Dianne Reeves for discussion on data element reuse and Denise Warzel for discussions on the cancer data standards repository.
Further Information

Publication History

Publication Date:
05 March 2018 (online)

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Summary

Objectives

Biomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.

Methods

To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.

Results

Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.

Conclusion

After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.

Keywords

Personalized medicine - genomic information models
 

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