Digital twins to personalize medicine.,Genome Medicine

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Digital twins to personalize medicine.
Genome Medicine ( IF 10.4 ) Pub Date : 2019-12-31 , DOI: 10.1186/s13073-019-0701-3
Bergthor Björnsson ₁ , Carl Borrebaeck ₂ , Nils Elander ₃ , Thomas Gasslander ₁ , Danuta R Gawel ₄ , Mika Gustafsson ₅ , Rebecka Jörnsten ₆ , Eun Jung Lee _{4,

7} , Xinxiu Li ₄ , Sandra Lilja ₄ , David Martínez-Enguita ₅ , Andreas Matussek _{8,

9} , Per Sandström ₁ , Samuel Schäfer ₄ , Margaretha Stenmarker _{10,

11} , X F Sun ₃ , Oleg Sysoev ₁₂ , Huan Zhang ₄ , Mikael Benson _{4,

13,

14} ,

Affiliation

Department of Surgery and Clinical and Experimental Medicine, Linköping University, 581 83, Linköping, Sweden.
Department of Immunotechnology, Lund University, Medicon Village, Scheelevägen, Lund, Sweden.
Departments of Oncology, and Clinical and Experimental Medicine, Linköping University, 581 83, Linköping, Sweden.
Centre for Personalized Medicine, Linköping University, 581 83, Linköping, Sweden.
Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden.
Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, 412 96, Gothenburg, Sweden.
Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, South Korea.
Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital, 141 52 Huddinge, Stockholm, Sweden.
Department of Laboratory Medicine, Region Jönköping County, Jönköping, Sweden.
Futurum-Academy for Health and Care, Department of Pediatrics, Region Jönköping County, Jönköping, Sweden.
Department of Pediatrics, Institution for Clinical Sciences, 413 90, Göteborg, Sweden.
Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 581 83, Linköping, Sweden.
Crown Princess Victoria Children's Hospital, 581 85, Linköping, Sweden.
Wallenberg Centre for Molecular Medicine, Linköping University, 581 83, Linköping, Sweden.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

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