Sadegh Marzban

This paper curates a large corpus of mathematical oncology literature using AI, revealing its evolution from cancer biology to therapeutic modeling over 140 years.

This paper develops a mechanistic learning framework to predict BCR::ABL1 fusion gene status and minimal residual disease using clinical data, finding that stem-like cell state is a strong predictor.

The paper identifies collagen remodeling as a mechanism that activates a GPCR-mediated immune checkpoint in ADGRG1+ CD8+ T cells, influencing response to immunotherapy.

This paper develops a mathematical model of cell state transitions in B-ALL to correlate with BCR::ABL1 status, MRD, and relapse risk using flow cytometry data.

This study uses computational modeling to explore how spatial interactions between immune cells and collagen fibers influence tumor growth and immune infiltration.

The paper introduces a hybrid PDE-ABM model to simulate viral dynamics in cell populations, showing it can predict SARS-CoV-2 and influenza infections and replicate experimental results.