Fractional Cancer-Informed Neural Networks For Colon Cancer Modeling

In this talk, we developed mathematical model to better understand the complexityof colon cancer cells and their metastatic mechanisms. The model is basedon a system of fractional-order di erential equations and Fractional-Cancer-InformedNeural Networks (FCINN). We used FCINN as an e ective deep learning tool for parameterestimation and numerical simulation of the model. Our ndings suggest thatthe most e ective factors in controlling the progression and preventing metastasis ofcolon cancer are the initial number of cancer cells, the inhibiting rates of tumor cellsby dendritic cells (DCs), the source of dendritic cells, and the activation of helperT-cells by dendritic cells.

کلیدواژه ها:

Mathematical model of fractional-order ، Colon cancer ، Immune system ، Deep learning ، Sensetivity analysis