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Found 4 papers, 1 papers with code
Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study
In this work, we develop a hierarchical Bayesian model of subpopulation dynamics that uses baseline covariate information to predict cancer dynamics under treatment, inspired by cancer dynamics in multiple myeloma (MM), where serum M protein is a well-known proxy of tumor burden.
A review of mechanistic learning in mathematical oncology
This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology.
A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy
Coupling discrete cell-based models with continuous models using hybrid cellular automata is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales.
Towards personalized computer simulations of breast cancer treatment
Cancer pathology is unique to a given individual, and developing personalized diagnostic and treatment protocols are a primary concern.
