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A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality
View ORCID ProfilePhilipp M. Altrock, Jeremy Ferlic, View ORCID ProfileTobias Galla, View ORCID ProfileMichael H. Tomasson, Franziska Michor
doi: https://doi.org/10.1101/208645
Philipp M. Altrock
1Department of Integrated Mathematical Oncology, Department of Malignant Hematology, and Department of Blood and Marrow Transplantation and Cellular Immunotherapy, Moffitt Cancer Center and Research Institute, Tampa, FL33612, USA
Jeremy Ferlic
2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Tobias Galla
3Theoretical Physics, The University of Manchester, Manchester M13 9PL, UK
Michael H. Tomasson
4Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA 52242, USA
Franziska Michor
2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
5Department of Stem Cell and Regenerative Biology, Harvard University, and The Broad Institute of Harvard and MIT, Cambridge, MA 02138, USA
6Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Article usage
Posted October 25, 2017.
A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality
Philipp M. Altrock, Jeremy Ferlic, Tobias Galla, Michael H. Tomasson, Franziska Michor
bioRxiv 208645; doi: https://doi.org/10.1101/208645
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