Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoi...

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Detalles Bibliográficos
Autores principales: Stahlberg, Eric A., Abdel-Rahman, Mohamed, Aguilar, Boris, Asadpoure, Alireza, Beckman, Robert A., Borkon, Lynn L., Bryan, Jeffrey N., Cebulla, Colleen M., Chang, Young Hwan, Chatterjee, Ansu, Deng, Jun, Dolatshahi, Sepideh, Gevaert, Olivier, Greenspan, Emily J., Hao, Wenrui, Hernandez-Boussard, Tina, Jackson, Pamela R., Kuijjer, Marieke, Lee, Adrian, Macklin, Paul, Madhavan, Subha, McCoy, Matthew D., Mohammad Mirzaei, Navid, Razzaghi, Talayeh, Rocha, Heber L., Shahriyari, Leili, Shmulevich, Ilya, Stover, Daniel G., Sun, Yi, Syeda-Mahmood, Tanveer, Wang, Jinhua, Wang, Qi, Zervantonakis, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586248/
https://www.ncbi.nlm.nih.gov/pubmed/36274654
http://dx.doi.org/10.3389/fdgth.2022.1007784

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586248/
https://www.ncbi.nlm.nih.gov/pubmed/36274654
http://dx.doi.org/10.3389/fdgth.2022.1007784

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