R. Gatenby

This paper introduces a method using logistic function symmetry to estimate tumor growth parameters from limited data, achieving faster and more accurate results than conventional regression approaches.

The paper investigates correlated gene expression evolution between dermal and endometrial fibroblasts across species and in an experimental model, finding significant correlations that support evolutionary links to cancer malignancy.

This paper applies deep reinforcement learning to adaptive drug scheduling in prostate cancer, showing it can extend time to progression by up to a quarter and outperform current protocols.

This study develops an adaptive therapy protocol for PARP inhibitors by combining mathematical modeling and experiments to optimize treatment scheduling and mitigate resistance.

The paper uses spatial-spectral analysis of immunohistochemistry data to model tumor growth dynamics via reaction-diffusion equations in breast cancer patients.

This paper introduces a model of cancer progression based on coordination games, exploring their potential in understanding and treating cancer.

The paper describes a high school internship program in mathematical oncology, detailing its structure, curriculum, and outcomes over five years.

The paper investigates how harsh microenvironments in early breast cancer select for a Warburg phenotype through metabolic and transcriptomic changes.

This study investigates whether a cost of resistance is necessary for adaptive therapy to delay cancer progression, finding that cell turnover modulates this requirement depending on tumor growth conditions.

This study shows that tumor acidity promotes prostate cancer by altering macrophage phenotype, both in vitro and in vivo, and is supported by in silico modeling.

This study identifies sex-specific predictors of survival in glioblastoma patients using MR imaging and clinical data.