Yearb Med Inform 2012; 21(01): 135-138
DOI: 10.1055/s-0038-1639444
Synopsis
Georg Thieme Verlag KG Stuttgart

Unlocking the Potential of Electronic Health Records for Translational Research

Findings from the Section on Bioinformatics and Translational Informatics
Y. L. Yip
1   Knowledge Management, Merck Serono International S.A., 9 Chemin des Mines, 1202 Geneva, Switzerland
,
Section Editor for the IMIA Yearbook Section on Bioinformatics and Translational Informatics › Author Affiliations
I would like to acknowledge the support of Martina Hutter and the reviewers in the selection process of the IMIA Yearbook.
Further Information

Correspondence to

Dr. Yum Lina Yip
Knowledge Management
Merck Serono S.A.
9 Chemin des Mines, Geneva
Switzerland

Publication History

Publication Date:
10 March 2018 (online)

 

Summary

Objectives

To review current excellent research and trend in the field of bioinformatics and translational informatics with direct application in the medical domain.

Method

Synopsis of the articles selected for the IMIA Yearbook 2012.

Results

Six excellent articles were selected in this Yearbook’s section on Bioinformatics and Translational Informatics. They exemplify current key advances in the use of patient information for translational research and health surveillance. First, two proof-of-concept studies demonstrated the cross-institutional and -geographic use of Electronic Health Records (EHR) for clinical trial subjects identification and drug safety signals detection. These reports pave ways to global large-scale population monitoring. Second, there is further evidence on the importance of coupling phenotypic information in EHR with genotypic information (either in biobank or in gene association studies) for new biomedical knowledge discovery. Third, patient data gathered via social media and self-reporting was found to be comparable to existent data and less labor intensive. This alternative means could potentially overcome data collection challenge in cohort and prospective studies. Finally, it can be noted that metagenomic studies are gaining momentum in bioinformatics and system-level analysis of human microbiome sheds important light on certain human diseases.

Conclusions

The current literature showed that the traditional bench to bedside translational research is increasing being complemented by the reverse approach, in which bedside information can be used to provide novel biomedical insights.


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  • References

  • 1 Yip YL. The promise of systems biology in clinical applications. Findings from the Yearbook 2008 Section in Bioinformatics. Yearb Med Inform 2008; 102-4.
  • 2 Ledford H. The full cycle. Nature 2008; 453: 843-5.
  • 3 Birdwell KA, Birdwell KA, Grady B, Choi L, Xu H, Bian A. et al. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenet Genomics 2012; 22: 32-42.
  • 4 Davis DA, Chawla NV. Exploring and exploiting disease interactions from multi-relational gene and phenotype network. PLoS One 2011; 06 (07) e22670.
  • 5 Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 2011; 07 (08) e1002141.
  • 6 Stamatakos GS, Georgiadi EC, Graf N, Kolokotroni EA, Dionysiou DD. Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaption of a multiscale cancer model. PLoS One 2011; 06 (03) e17594.
  • 7 Jing X, Kay S, Marley T, Hardiker NR, Cimino JJ. Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard. J Biomed Inform 2012; 45 (01) 82-92.
  • 8 Anderson N, Abend A, Mandel A, Geraghty E, Gabriel D, Wynden R. et al. Implementation of a deidentified federated data network for population-based cohort discovery. J Am Med Inform Assoc. 2011 Epub
  • 9 Coloma PM, Schuemie MJ, Trifiro G, Gini R, Herings R, Hippisley-Cox J. et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf 2011; 20: 1-11.
  • 10 Greenblum S, Turnbaugh PJ, Borenstein E. Metagenomic systems biology of the human gut microbiome revieals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci USA 2012; 109 (02) 594-9.
  • 11 Yip YL. Genome, and beyond. Section in Bioinformatics. Findings from the Yearbook 2011; 06 (01) 156-9.
  • 12 Kohane IS, Churchill SE, Murphy SN. A translational engine at the national scale: informatics for integrating biology and the bedside. J Am Med Inform Assoc 2012; 19 (02) 181-5.
  • 13 Weitzmann ER, Adida B, Kelemen S, Mandl KD. Sharing data for public health research by members of an international online diabetes social network. PLoS One 2011; 06 (04) e19256.

Correspondence to

Dr. Yum Lina Yip
Knowledge Management
Merck Serono S.A.
9 Chemin des Mines, Geneva
Switzerland

  • References

  • 1 Yip YL. The promise of systems biology in clinical applications. Findings from the Yearbook 2008 Section in Bioinformatics. Yearb Med Inform 2008; 102-4.
  • 2 Ledford H. The full cycle. Nature 2008; 453: 843-5.
  • 3 Birdwell KA, Birdwell KA, Grady B, Choi L, Xu H, Bian A. et al. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenet Genomics 2012; 22: 32-42.
  • 4 Davis DA, Chawla NV. Exploring and exploiting disease interactions from multi-relational gene and phenotype network. PLoS One 2011; 06 (07) e22670.
  • 5 Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 2011; 07 (08) e1002141.
  • 6 Stamatakos GS, Georgiadi EC, Graf N, Kolokotroni EA, Dionysiou DD. Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaption of a multiscale cancer model. PLoS One 2011; 06 (03) e17594.
  • 7 Jing X, Kay S, Marley T, Hardiker NR, Cimino JJ. Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard. J Biomed Inform 2012; 45 (01) 82-92.
  • 8 Anderson N, Abend A, Mandel A, Geraghty E, Gabriel D, Wynden R. et al. Implementation of a deidentified federated data network for population-based cohort discovery. J Am Med Inform Assoc. 2011 Epub
  • 9 Coloma PM, Schuemie MJ, Trifiro G, Gini R, Herings R, Hippisley-Cox J. et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf 2011; 20: 1-11.
  • 10 Greenblum S, Turnbaugh PJ, Borenstein E. Metagenomic systems biology of the human gut microbiome revieals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci USA 2012; 109 (02) 594-9.
  • 11 Yip YL. Genome, and beyond. Section in Bioinformatics. Findings from the Yearbook 2011; 06 (01) 156-9.
  • 12 Kohane IS, Churchill SE, Murphy SN. A translational engine at the national scale: informatics for integrating biology and the bedside. J Am Med Inform Assoc 2012; 19 (02) 181-5.
  • 13 Weitzmann ER, Adida B, Kelemen S, Mandl KD. Sharing data for public health research by members of an international online diabetes social network. PLoS One 2011; 06 (04) e19256.