Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata
Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata
Abstract De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.
Flores Elsa R.、Gatenbee Chandler、Kim Eunjung、Harris Valerie、Cross William、Schenck Ryan、Cho Heyrim、McKenna Joseph、Coker Elizabeth、Tsai Steven Lee-Kramer Kenneth Y.、West Jeffrey
Department of Molecular Oncology, Moffitt Cancer CenterDepartment of Integrated Mathematical Oncology, Moffitt Cancer CenterDepartment of Integrated Mathematical Oncology, Moffitt Cancer CenterCenter for Evolution and Cancer, Arizona State UniversityEvolution and Cancer Laboratory, Barts Cancer InstituteDepartment of Integrated Mathematical Oncology, Moffitt Cancer Center||Wellcome Trust Centre for Human Genetics, University of OxfordDepartment of Mathematics, University of MarylandNational Institutes of HealthInstitute of Cancer ResearchDepartments of Anatomic Pathology & Tumor Biology, Moffitt Cancer CenterDepartment of Integrated Mathematical Oncology, Moffitt Cancer Center||Department of Aerospace and Mechanical Engineering, University of Southern California
医学研究方法肿瘤学细胞生物学
Flores Elsa R.,Gatenbee Chandler,Kim Eunjung,Harris Valerie,Cross William,Schenck Ryan,Cho Heyrim,McKenna Joseph,Coker Elizabeth,Tsai Steven Lee-Kramer Kenneth Y.,West Jeffrey.Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata[EB/OL].(2025-03-28)[2025-04-24].https://www.biorxiv.org/content/10.1101/211383.点此复制
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