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J. Pers. Med. 2017, 7(3), 9; https://doi.org/10.3390/jpm7030009

Personalized Computational Models as Biomarkers

1,2,3,* and 1,*
1
Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
2
Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
3
School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
*
Authors to whom correspondence should be addressed.
Academic Editor: Stephen B. Liggett
Received: 20 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
  |  
PDF [175 KB, uploaded 1 September 2017]

Abstract

Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but biomarkers based on these technologies often lack the dynamic element to follow the progression of a disease or response to therapy. Here, we discuss computational models as a new conceptual approach to biomarker discovery and design. Being able to integrate a large amount of information, including dynamic information, computational models can simulate disease evolution and response to therapy with high sensitivity and specificity. By populating these models with personal data, they can be highly individualized and will provide a powerful new tool in the armory of personalized medicine. View Full-Text
Keywords: biomarkers; neuroblastoma; mathematical/computational modelling; personalized medicine biomarkers; neuroblastoma; mathematical/computational modelling; personalized medicine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kolch, W.; Fey, D. Personalized Computational Models as Biomarkers. J. Pers. Med. 2017, 7, 9.

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