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Cited by 588Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Digital twins to personalize medicine

Open access
on behalf of the Swedish Digital Twin Consortium, Björnsson, Bergthor, Borrebaeck, Carl, Elander, Nils, Gasslander, Thomas, Gawel, Danuta R., Gustafsson, Mika, Jörnsten, Rebecka, Lee, Eun Jung, Li, Xinxiu, Lilja, Sandra, Martínez-Enguita, David, Matussek, Andreas, Sandström, Per, Schäfer, Samuel, Stenmarker, Margaretha, Sun, X. F., Sysoev, Oleg, Zhang, Huan, Benson, Mikael·2020·Genome Medicine
DOI 10.1186/s13073-019-0701-3PMC PMC6938608
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Abstract

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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Fig. 1 The digital twin concept for personalized medicine. a An individual patient has a local sign of disease (red). b A digital twin of this patient is constructed in unlimited copies, based on computational network models of thousands of disease-relevant variables. c Each twin is computationally treated with one or more of the thousands of drugs. This results in digital cure of one patient (green). d The drug that has the best effect on the digital twin is selected for treatment of the patient
Fig. 1 The digital twin concept for personalized medicine. a An individual patient has a local sign of disease (red). b A digital twin of this patient is constructed in unlimited copies, based on computational network models of thousands of disease-relevant variables. c Each twin is computationally treated with one or more of the thousands of drugs. This results in digital cure of one patient (green). d The drug that has the best effect on the digital twin is selected for treatment of the patientCC BY · PubMed Central

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Research works printed but not linked: 1

  1. US Food and Drug Administration. Paving the way for personalized medicine: FDA’s role in a new era of medical product development. Silver Spring: US Food and Drug Administration; 2013. https://www.fdanews.com/ext/resources/files/10/10-28-13-Personalized-Medicine.pdf. Accessed 26 Nov 2019.

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Metadata

Authors
on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., Lilja, S., Martínez-Enguita, D., Matussek, A., Sandström, P., Schäfer, S., Stenmarker, M., Sun, X. F., Sysoev, O., Zhang, H., & Benson, M.
Affiliations
  • Lund University (SE)
  • Linköping University (SE)
  • Chalmers University of Technology (SE)
  • Karolinska University Hospital (SE)
  • Futurum - Akademin för Hälsa och Vård (SE)
Corresponding author
Benson, Mikael
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2019-12-31
Volume
12
Issue
1
Pages
4-4
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-019-0701-3
PMC
PMC6938608
OpenAlex
W2999365564
Cited by
566
References
9
Citations by year
2026: 143 · 2025: 126 · 2024: 103 · 2023: 79 · 2022: 66 · 2021: 37 · 2020: 12
FWCI
19.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DiVA (Linkoping University) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Lund University Publications (Lund University) (submittedVersion)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
Swedish Cancer Foundation
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
digital twin, personalized medicine, patient modeling, drug repurposing, computational drug screening, precision medicine, data integration
MeSH terms
Disease, Humans, Databases, Factual, Neural Networks, Computer, Precision Medicine

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