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  • Modeling the T cell immune response: a fascinating challenge
    Three main stages of an immune response can be characterized; 1) recognition i e is the perceived threat a danger to the organism?; 2) removal of the pathogen that is achieved via a variety of effector mechanisms and 3) return to homeostasis which involves contraction of expanded immune cell populations and repair of damaged tissues
  • Supervised learning methods in modeling of CD4+ T cell . . .
    This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR)
  • The dynamic lives of T cells: new approaches and themes
    In this review, we focus on three approaches for assessing the dynamic behaviors of T cells that use observations from both global and local frames of reference to generate models of how T cells transition between different states
  • Studying the importance of regulatory T cells in . . .
    In this paper, the key role of Regulatory T cells is discussed and a model in ordinary differential equation (ODE) form is proposed by adding this state to the system dynamics considering chemoimmunotherapy treatment Regulatory T cells are considered as one of the main tumor cells’ tactics to deceive the body’s immune system
  • Phenotypic Approaches to T Cell Activation: A Comparative . . .
    Our sensitivity analysis identified phosphorylation rate as a key parameter influencing most model outcomes This study highlights the strengths and limitations of current T-cell activation models, suggesting improvements to enhance their predictive accuracy in future research
  • Mapping T cell dynamics to molecular profiles through . . .
    These experimental models combine T cells and tumor cells to simulate T cell behavior and cancer targeting potential, offering insight into the mode of action of immunotherapy and





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