Open Access
Yearb Med Inform 2012; 21(01): 117-125
DOI: 10.1055/s-0038-1639441
Working Group Contribution
Georg Thieme Verlag KG Stuttgart

Data Integration in Genomic Medicine: Trends and Applications

Contribution of the IMIA Working Group on Informatics in Genomic Medicine
J. A. Seoane
1   IMIA Informatics in Genomic Medicine Working Group Chair, Information and Communications Technologies Department, School of Computer Science, University of A Coruña A Coruña, Spain
,
J. Dorado
1   IMIA Informatics in Genomic Medicine Working Group Chair, Information and Communications Technologies Department, School of Computer Science, University of A Coruña A Coruña, Spain
,
V. Aguiar-Pulido
1   IMIA Informatics in Genomic Medicine Working Group Chair, Information and Communications Technologies Department, School of Computer Science, University of A Coruña A Coruña, Spain
,
A. Pazos
1   IMIA Informatics in Genomic Medicine Working Group Chair, Information and Communications Technologies Department, School of Computer Science, University of A Coruña A Coruña, Spain
› Author Affiliations

This work is supported by the following projects: “Ibero-American Network of the Nano-Bio-Info-Cogno Convergent Technologies”, Ibero-NBIC Network (209RT-0366) funded by CYTED (Spain), “Development of new image analysis techniques in 2D Gel for biomedical research” (ref.10SIN105004PR) funded by Xunta de Galicia and RD07/0067/0005, funded by the Carlos III Health Institute.
Further Information

Publication History

Publication Date:
10 March 2018 (online)

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Summary

Objectives

In a near future, each person will incorporate his/her own sequenced genome in his/her electronic health record. In that precise moment, genomic medicine will be fundamental for clinical practice, as an essential key of personalized medicine. All the genomic data, as well as other ‘omics’ and clinical data necessary for personalized medicine, are stored in several distributed databases. Research and patient care require each time more biomedical data integration of several distributed heterogeneous datasources.

Methods

This work develops a comprehensive review of the most relevant works in biomedical data integration, specifically in genomic medical data, analyzing the evolution of architecture and integration techniques during the last 20 years, and its usage.

Conclusion

Most of these solutions, based on cross-linking, data warehouse or federated approaches, are suitable for specific domains. However, none of the models found in the literature is completely appropriate for a general biomedical data integration problem.