bioRxivCancer BiologyPreprint
Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata
Eunjung Kim, Ryan Schenck, Jeffrey WestiD, William Cross, Valerie Harris, Joseph McKenna, Heyrim ChoiD, Elizabeth Coker, Steven Lee-Kramer Kenneth Y. Tsai, Elsa R. Flores, Chandler Gatenbee
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.
- organism: human
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
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