Yearb Med Inform 2015; 24(01): 164-169
DOI: 10.15265/IY-2015-005
Original Article
Georg Thieme Verlag KG Stuttgart

From Molecules to Patients: The Clinical Applications of Translational Bioinformatics

K. Regan
1   The Ohio State University, Department of Biomedical Informatics, Columbus, OH, USA
,
P.R.O. Payne
1   The Ohio State University, Department of Biomedical Informatics, Columbus, OH, USA
› Author Affiliations
Further Information

Correspondence to:

Philip R.O. Payne, PhD, FACMI
The Ohio State University
Department of Biomedical Informatics
250 Lincoln Tower
1800 Cannon Drive
Columbus, OH 43210, USA
Phone: +1 614 292 4778   

Publication History

13 August 2015

Publication Date:
10 March 2018 (online)

 

Summary

Objective: In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research.

Methods: Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine.

Results: Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systems-level approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues.

Conclusions: There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci – domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.


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  • References

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  • 2 Weston AD, Hood L. Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative, and Personalized Medicine. J Proteome Res 2004; March 3 (02) 179-96.
  • 3 Altman RB. Chapter 2: Introduction to Translational Bioinformatics Collection. PLoS Comput Biol 2012; 8 (12) e1002796.
  • 4 Kim JH. New horizons in translational bioinformatics: TBC 2013. BMC Med Genomics 2014; 7 (01) l1.
  • 5 Capriotti E, Nehrt NL, Kann MG, Bromberg Y. Bioinformatics for personal genome interpretation. Brief Bioinform 2012; 13 (04) 495-512.
  • 6 Simon R, Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 2013; 12 (05) 358-69.
  • 7 Payne PRO. Chapter 1: Biomedical Knowledge Integration. PLoS Comput Biol 2012; 8 (12) e1002826. doi:10.1371/journal.pcbi.1002826.
  • 8 Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J. Some experiences and opportunities for big data in translational research. Genet Med 2013; 15 (10) 802-9.
  • 9 Chen J, Qian F, Yan W, Shen B. Translational biomedical informatics in the cloud: present and future. BioMed Res Int 2013; 2013: 658925. doi: 10.1155/2013/658925.
  • 10 Shah NH, Cole T, Musen MA. Chapter 9: Analyses Using Disease Ontologies. PLoS Comput Biol 2012; 8 (12) e1002827. doi:10.1371/journal. pcbi.1002827.
  • 11 Gonzalez MW, Kann MG. Chapter 4: Protein Interactions and Disease. PLoS Comput Biol 2012; 8 (12) e1002819.
  • 12 Bebek G, Koyutürk M, Price DN, Chance MR. Network biology methods integrating biological data for translational science. Brief Bioinform 2012; 13 (04) 446-59.
  • 13 Sarkar IN. A vector space model approach to identify genetically related diseases. J Am Med Inform Assoc 2012; 19 (02) 249-54.
  • 14 Bhavnani SK, Abbas M, McMicken V, Oezguen N, Tupa J. iCircos: Visual Analytics for Translational Bioinformatics. IHI’12 Proceedings of the 2nd ACM International Health Informatics Symposium 2012; 679-84.
  • 15 Lam TK, Spitz M, Schully SD, Khoury MJ. “Drivers” of translational cancer epidemiology in the 21st century: needs and opportunities. Cancer Epidemiol Biomarkers Prev 2013; 22 (02) 181-8.
  • 16 Deyati A, Younesi E, Hofmann-Apitius M, Novac N. Challenges and opportunities for oncology bio-marker discovery. Drug Discov Today 2013; 18 13-14 614-24.
  • 17 Chen J, Zhang D, Yan W, Yang D, Shen B. Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era. BioMed Res Int 2013; 2013: 901578.
  • 18 Lesko LJ. Drug Research and Translational Bioinformatics. Clin Pharmacol Ther 2012; 91 (06) 960-2.
  • 19 Butte AJ, Ito S. Translational Bioinformatics: Data-driven Drug Discovery and Development. Clin Pharmacol Ther 2012; 91 (06) 949-52.
  • 20 Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 2013; 93 (04) 335-41.
  • 21 LePendu P, Liu Y, Iyer S, Udell MR, Shah NH. Analyzing Patterns of Drug Use in Clinical Notes for Patient Safety. AMIA Jt Summits Transl Sci Proc 2012; 2012: 63-70.
  • 22 Duke JD, Han X, Wang Z, Subhadarshini A, Karnik SD, Li X. et al. Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions. PLoS Comput Biol 2012; 8 (08) e1002614. doi:10.1371/journal. pcbi.1002614.
  • 23 Johnson DE, Sudarsanam S, Bingham J, Srinivasan S. Translational Biology Approach to Identify Causative Factors for Rare Toxicities in Humans and Animals. Curr Drug Discov Technol 2012; 9 (01) 77-80.
  • 24 Azuaje F. Drug interaction networks: an introduction to translational and clinical applications. Cardiovasc Res 2013; 97 (04) 631-41.
  • 25 Dalpé G, Joly Y. Opportunities and challenges provided by cloud repositories for bioinformatics-enabled drug discovery. Drug Development Research 2014; 75 (06) 393-401.
  • 26 Mirnezami R, Nicholson J, Darzi A. Preparing for Precision Medicine. N Engl J Med 2012; 366 (06) 489-91.
  • 27 Tarczy-Hornoch P, Amendola L, Aronson SJ, Garrawy L, Gray S, Grundmeier RW. et al. A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record. Genet Med 2013; 15 (10) 824-32.
  • 28 Suh KS, Sarojini S, Youssif M, Nalley K, Milinovikj N, Elloumi F. et al. Tissue banking, bioinformatics, and electronic medical records: the front-end requirements for personalized medicine. J Oncol 2013; 2013: 368751.

Correspondence to:

Philip R.O. Payne, PhD, FACMI
The Ohio State University
Department of Biomedical Informatics
250 Lincoln Tower
1800 Cannon Drive
Columbus, OH 43210, USA
Phone: +1 614 292 4778   

  • References

  • 1 Butte AJ. Translational bioinformatics: coming of age. J Am Med Inform Assoc 2008; 15 (06) 709-14.
  • 2 Weston AD, Hood L. Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative, and Personalized Medicine. J Proteome Res 2004; March 3 (02) 179-96.
  • 3 Altman RB. Chapter 2: Introduction to Translational Bioinformatics Collection. PLoS Comput Biol 2012; 8 (12) e1002796.
  • 4 Kim JH. New horizons in translational bioinformatics: TBC 2013. BMC Med Genomics 2014; 7 (01) l1.
  • 5 Capriotti E, Nehrt NL, Kann MG, Bromberg Y. Bioinformatics for personal genome interpretation. Brief Bioinform 2012; 13 (04) 495-512.
  • 6 Simon R, Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 2013; 12 (05) 358-69.
  • 7 Payne PRO. Chapter 1: Biomedical Knowledge Integration. PLoS Comput Biol 2012; 8 (12) e1002826. doi:10.1371/journal.pcbi.1002826.
  • 8 Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J. Some experiences and opportunities for big data in translational research. Genet Med 2013; 15 (10) 802-9.
  • 9 Chen J, Qian F, Yan W, Shen B. Translational biomedical informatics in the cloud: present and future. BioMed Res Int 2013; 2013: 658925. doi: 10.1155/2013/658925.
  • 10 Shah NH, Cole T, Musen MA. Chapter 9: Analyses Using Disease Ontologies. PLoS Comput Biol 2012; 8 (12) e1002827. doi:10.1371/journal. pcbi.1002827.
  • 11 Gonzalez MW, Kann MG. Chapter 4: Protein Interactions and Disease. PLoS Comput Biol 2012; 8 (12) e1002819.
  • 12 Bebek G, Koyutürk M, Price DN, Chance MR. Network biology methods integrating biological data for translational science. Brief Bioinform 2012; 13 (04) 446-59.
  • 13 Sarkar IN. A vector space model approach to identify genetically related diseases. J Am Med Inform Assoc 2012; 19 (02) 249-54.
  • 14 Bhavnani SK, Abbas M, McMicken V, Oezguen N, Tupa J. iCircos: Visual Analytics for Translational Bioinformatics. IHI’12 Proceedings of the 2nd ACM International Health Informatics Symposium 2012; 679-84.
  • 15 Lam TK, Spitz M, Schully SD, Khoury MJ. “Drivers” of translational cancer epidemiology in the 21st century: needs and opportunities. Cancer Epidemiol Biomarkers Prev 2013; 22 (02) 181-8.
  • 16 Deyati A, Younesi E, Hofmann-Apitius M, Novac N. Challenges and opportunities for oncology bio-marker discovery. Drug Discov Today 2013; 18 13-14 614-24.
  • 17 Chen J, Zhang D, Yan W, Yang D, Shen B. Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era. BioMed Res Int 2013; 2013: 901578.
  • 18 Lesko LJ. Drug Research and Translational Bioinformatics. Clin Pharmacol Ther 2012; 91 (06) 960-2.
  • 19 Butte AJ, Ito S. Translational Bioinformatics: Data-driven Drug Discovery and Development. Clin Pharmacol Ther 2012; 91 (06) 949-52.
  • 20 Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 2013; 93 (04) 335-41.
  • 21 LePendu P, Liu Y, Iyer S, Udell MR, Shah NH. Analyzing Patterns of Drug Use in Clinical Notes for Patient Safety. AMIA Jt Summits Transl Sci Proc 2012; 2012: 63-70.
  • 22 Duke JD, Han X, Wang Z, Subhadarshini A, Karnik SD, Li X. et al. Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions. PLoS Comput Biol 2012; 8 (08) e1002614. doi:10.1371/journal. pcbi.1002614.
  • 23 Johnson DE, Sudarsanam S, Bingham J, Srinivasan S. Translational Biology Approach to Identify Causative Factors for Rare Toxicities in Humans and Animals. Curr Drug Discov Technol 2012; 9 (01) 77-80.
  • 24 Azuaje F. Drug interaction networks: an introduction to translational and clinical applications. Cardiovasc Res 2013; 97 (04) 631-41.
  • 25 Dalpé G, Joly Y. Opportunities and challenges provided by cloud repositories for bioinformatics-enabled drug discovery. Drug Development Research 2014; 75 (06) 393-401.
  • 26 Mirnezami R, Nicholson J, Darzi A. Preparing for Precision Medicine. N Engl J Med 2012; 366 (06) 489-91.
  • 27 Tarczy-Hornoch P, Amendola L, Aronson SJ, Garrawy L, Gray S, Grundmeier RW. et al. A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record. Genet Med 2013; 15 (10) 824-32.
  • 28 Suh KS, Sarojini S, Youssif M, Nalley K, Milinovikj N, Elloumi F. et al. Tissue banking, bioinformatics, and electronic medical records: the front-end requirements for personalized medicine. J Oncol 2013; 2013: 368751.