RT Journal Article
SR Electronic
T1 Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 211383
DO 10.1101/211383
A1 Eunjung Kim
A1 Ryan Schenck
A1 Jeffrey West
A1 William Cross
A1 Valerie Harris
A1 Joseph McKenna
A1 Heyrim Cho
A1 Elizabeth Coker
A1 Steven Lee-Kramer Kenneth Y. Tsai
A1 Elsa R. Flores
A1 Chandler Gatenbee
YR 2017
UL http://biorxiv.org/content/early/2017/11/11/211383.abstract
AB 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.