Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we...

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Detalles Bibliográficos
Autores principales: Jørgensen, Andreas Christ Sølvsten, Hill, Ciaran Scott, Sturrock, Marc, Tang, Wenhao, Karamched, Saketh R., Gorup, Dunja, Lythgoe, Mark F., Parrinello, Simona, Marguerat, Samuel, Shahrezaei, Vahid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Mathematics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031411/
https://www.ncbi.nlm.nih.gov/pubmed/36968241
http://dx.doi.org/10.1098/rsos.221444

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031411/
https://www.ncbi.nlm.nih.gov/pubmed/36968241
http://dx.doi.org/10.1098/rsos.221444

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