As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer
evolution has improved the understanding of anticancer treatment resistance. A better …

In this paper, we apply the q-homotopy analysis transform method to the mathematical
model of the cancer chemotherapy effect in the sense of Caputo fractional. We find some …

We briefly review spatially homogeneous mechanistic mathematical models describing the
interactions between a malignant tumor and the immune system. We begin with the simplest …

Mathematical models of tumor-immune interactions provide an analytic framework in which
to address specific questions about tumor-immune dynamics. We present a new …

We develop and analyze a mathematical model, in the form of a system of ordinary
differential equations (ODEs), governing cancer growth on a cell population level with …

Since the 1990s, state-dependent Riccati equation (SDRE) strategies have emerged as
general design methods that provide a systematic and effective means of designing …

In the tumour microenvironment, cancer cells directly interact with both the immune system
and the stroma. It is firmly established that the immune system, historically believed to be a …

The book aims to provide an introduction to mathematical models that describe the
dynamics of tumor growth and the evolution of tumor cells. It can be used as a textbook for …

We present a hybrid cellular automata–partial differential equation model of moderate
complexity to describe the interactions between a growing tumor next to a nutrient source …