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Digital twins for predictive oncology will be a paradigm shift for precision cancer care
Nat Med
.
2021 Dec;27(12):2065-2066.
doi: 10.1038/s41591-021-01558-5.
Authors
Tina Hernandez-Boussard
#
1
,
Paul Macklin
#
2
,
Emily J Greenspan
#
3
,
Amy L Gryshuk
4
,
Eric Stahlberg
5
,
Tanveer Syeda-Mahmood
6
,
Ilya Shmulevich
7
Affiliations
1
Department of Medicine, Stanford University, Stanford, CA, USA. boussard@stanford.edu.
2
Department of Medicine, Indiana University, Stanford, CA, USA.
3
Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
4
Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA.
5
Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
6
IBM Almaden Research Center, San Jose, CA, USA.
7
Institute for Systems Biology, Seattle, WA, USA.
#
Contributed equally.
PMID:
34824458
PMCID:
PMC9097784
DOI:
10.1038/s41591-021-01558-5
No abstract available
Publication types
Letter
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
Computer Simulation
Humans
Medical Oncology
Neoplasms / therapy*
Precision Medicine*
Grants and funding
75N91019D00024/CA/NCI NIH HHS/United States
R01 CA183962/CA/NCI NIH HHS/United States