@article{mathoncoRoadmap,
  title={The 2019 Mathematical Oncology Roadmap},
  author={Russell C Rockne and Andrea Hawkins-Daarud and Kristin R Swanson and James P Sluka and James A Glazier and Paul Macklin and David A Hormuth and Angela M Jarrett and Ernesto A B F Lima and J Tinsley Oden and George Biros and Thomas E Yankeelov and Kit Curtius and Ibrahim Al Bakir and Dominik Wodarz and Natalia Komarova and Luis Aparicio and Mykola Bordyuh and Raul Rabadan and Stacey D Finley and Heiko Enderling and Jimmy Caudell and Eduardo G Moros and Alexander R A Anderson and Robert A Gatenby and Artem Kaznatcheev and Peter Jeavons and Nikhil Krishnan and Julia Pelesko and Raoul R Wadhwa and Nara Yoon and Daniel Nichol and Andriy Marusyk and Michael Hinczewski and Jacob G Scott},
  journal={Physical {B}iology},
  volume={16},
  number={4},
  pages={041005},
  year={2019},
  publisher={IOP Publishing},
  abstract={Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.}
}

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G"],"bibdata":{"bibtype":"article","type":"article","title":"The 2019 Mathematical Oncology Roadmap","author":[{"firstnames":["Russell","C"],"propositions":[],"lastnames":["Rockne"],"suffixes":[]},{"firstnames":["Andrea"],"propositions":[],"lastnames":["Hawkins-Daarud"],"suffixes":[]},{"firstnames":["Kristin","R"],"propositions":[],"lastnames":["Swanson"],"suffixes":[]},{"firstnames":["James","P"],"propositions":[],"lastnames":["Sluka"],"suffixes":[]},{"firstnames":["James","A"],"propositions":[],"lastnames":["Glazier"],"suffixes":[]},{"firstnames":["Paul"],"propositions":[],"lastnames":["Macklin"],"suffixes":[]},{"firstnames":["David","A"],"propositions":[],"lastnames":["Hormuth"],"suffixes":[]},{"firstnames":["Angela","M"],"propositions":[],"lastnames":["Jarrett"],"suffixes":[]},{"firstnames":["Ernesto","A","B","F"],"propositions":[],"lastnames":["Lima"],"suffixes":[]},{"firstnames":["J","Tinsley"],"propositions":[],"lastnames":["Oden"],"suffixes":[]},{"firstnames":["George"],"propositions":[],"lastnames":["Biros"],"suffixes":[]},{"firstnames":["Thomas","E"],"propositions":[],"lastnames":["Yankeelov"],"suffixes":[]},{"firstnames":["Kit"],"propositions":[],"lastnames":["Curtius"],"suffixes":[]},{"firstnames":["Ibrahim","Al"],"propositions":[],"lastnames":["Bakir"],"suffixes":[]},{"firstnames":["Dominik"],"propositions":[],"lastnames":["Wodarz"],"suffixes":[]},{"firstnames":["Natalia"],"propositions":[],"lastnames":["Komarova"],"suffixes":[]},{"firstnames":["Luis"],"propositions":[],"lastnames":["Aparicio"],"suffixes":[]},{"firstnames":["Mykola"],"propositions":[],"lastnames":["Bordyuh"],"suffixes":[]},{"firstnames":["Raul"],"propositions":[],"lastnames":["Rabadan"],"suffixes":[]},{"firstnames":["Stacey","D"],"propositions":[],"lastnames":["Finley"],"suffixes":[]},{"firstnames":["Heiko"],"propositions":[],"lastnames":["Enderling"],"suffixes":[]},{"firstnames":["Jimmy"],"propositions":[],"lastnames":["Caudell"],"suffixes":[]},{"firstnames":["Eduardo","G"],"propositions":[],"lastnames":["Moros"],"suffixes":[]},{"firstnames":["Alexander","R","A"],"propositions":[],"lastnames":["Anderson"],"suffixes":[]},{"firstnames":["Robert","A"],"propositions":[],"lastnames":["Gatenby"],"suffixes":[]},{"firstnames":["Artem"],"propositions":[],"lastnames":["Kaznatcheev"],"suffixes":[]},{"firstnames":["Peter"],"propositions":[],"lastnames":["Jeavons"],"suffixes":[]},{"firstnames":["Nikhil"],"propositions":[],"lastnames":["Krishnan"],"suffixes":[]},{"firstnames":["Julia"],"propositions":[],"lastnames":["Pelesko"],"suffixes":[]},{"firstnames":["Raoul","R"],"propositions":[],"lastnames":["Wadhwa"],"suffixes":[]},{"firstnames":["Nara"],"propositions":[],"lastnames":["Yoon"],"suffixes":[]},{"firstnames":["Daniel"],"propositions":[],"lastnames":["Nichol"],"suffixes":[]},{"firstnames":["Andriy"],"propositions":[],"lastnames":["Marusyk"],"suffixes":[]},{"firstnames":["Michael"],"propositions":[],"lastnames":["Hinczewski"],"suffixes":[]},{"firstnames":["Jacob","G"],"propositions":[],"lastnames":["Scott"],"suffixes":[]}],"journal":"Physical Biology","volume":"16","number":"4","pages":"041005","year":"2019","publisher":"IOP Publishing","abstract":"Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.","bibtex":"@article{mathoncoRoadmap,\r\n title={The 2019 Mathematical Oncology Roadmap},\r\n author={Russell C Rockne and Andrea Hawkins-Daarud and Kristin R Swanson and James P Sluka and James A Glazier and Paul Macklin and David A Hormuth and Angela M Jarrett and Ernesto A B F Lima and J Tinsley Oden and George Biros and Thomas E Yankeelov and Kit Curtius and Ibrahim Al Bakir and Dominik Wodarz and Natalia Komarova and Luis Aparicio and Mykola Bordyuh and Raul Rabadan and Stacey D Finley and Heiko Enderling and Jimmy Caudell and Eduardo G Moros and Alexander R A Anderson and Robert A Gatenby and Artem Kaznatcheev and Peter Jeavons and Nikhil Krishnan and Julia Pelesko and Raoul R Wadhwa and Nara Yoon and Daniel Nichol and Andriy Marusyk and Michael Hinczewski and Jacob G Scott},\r\n journal={Physical {B}iology},\r\n volume={16},\r\n number={4},\r\n pages={041005},\r\n year={2019},\r\n publisher={IOP Publishing},\r\n abstract={Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.}\r\n}\r\n\r\n","author_short":["Rockne, R. C","Hawkins-Daarud, A.","Swanson, K. R","Sluka, J. P","Glazier, J. A","Macklin, P.","Hormuth, D. A","Jarrett, A. M","Lima, E. A B F","Oden, J T.","Biros, G.","Yankeelov, T. E","Curtius, K.","Bakir, I. A.","Wodarz, D.","Komarova, N.","Aparicio, L.","Bordyuh, M.","Rabadan, R.","Finley, S. D","Enderling, H.","Caudell, J.","Moros, E. G","Anderson, A. R A","Gatenby, R. A","Kaznatcheev, A.","Jeavons, P.","Krishnan, N.","Pelesko, J.","Wadhwa, R. R","Yoon, N.","Nichol, D.","Marusyk, A.","Hinczewski, M.","Scott, J. G"],"key":"mathoncoRoadmap","id":"mathoncoRoadmap","bibbaseid":"rockne-hawkinsdaarud-swanson-sluka-glazier-macklin-hormuth-jarrett-etal-the2019mathematicaloncologyroadmap-2019","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"http://kaznatcheev.github.io/KazPubs.bib","dataSources":["gioZQWeA8bj8k8pid"],"keywords":[],"search_terms":["2019","mathematical","oncology","roadmap","rockne","hawkins-daarud","swanson","sluka","glazier","macklin","hormuth","jarrett","lima","oden","biros","yankeelov","curtius","bakir","wodarz","komarova","aparicio","bordyuh","rabadan","finley","enderling","caudell","moros","anderson","gatenby","kaznatcheev","jeavons","krishnan","pelesko","wadhwa","yoon","nichol","marusyk","hinczewski","scott"],"title":"The 2019 Mathematical Oncology Roadmap","year":2019}