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Related Concept Videos

Tumor Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Predictive Immune Modeling of Solid Tumors
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Intelligent solution predictive networks for non-linear tumor-immune delayed model.

Nabeela Anwar1, Iftikhar Ahmad1, Adiqa Kausar Kiani2

  • 1Department of Mathematics, University of Gujrat, Gujrat, Pakistan.

Computer Methods in Biomechanics and Biomedical Engineering
|June 23, 2023
PubMed
Summary

This study analyzes a non-linear tumor-immune delayed (TID) model using neural networks with backpropagation Levenberg-Marquardt (NNLMA). The NNLMA effectively models tumor-immune dynamics, showing high accuracy and reliability.

Keywords:
Tumor-immune delayed modeldelayed differential systemsexplicit Runge-Kutta methodlevenberg marquardt approachneural networksregression measuressoft computing paradigm

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Area of Science:

  • Mathematical Biology
  • Computational Biology
  • Immunology

Background:

  • Tumor growth and immune system interactions are complex processes.
  • Time delays in biological systems significantly impact model dynamics.
  • Understanding these dynamics is crucial for developing effective cancer therapies.

Purpose of the Study:

  • To analyze the dynamics of a non-linear tumor-immune delayed (TID) model.
  • To apply a soft computing paradigm, specifically neural networks with backpropagation Levenberg-Marquardt approach (NNLMA), for solving the TID model.
  • To validate the efficacy of the NNLMA in accurately representing tumor-immune interactions.

Main Methods:

  • Formulation of a non-linear TID model using delay ordinary differential equations.
  • Generation of baseline data using the explicit Runge-Kutta method (RKM).
  • Application of NNLMA with data subdivision for training, testing, and validation.

Main Results:

  • The NNLMA successfully approximated the solution of the non-linear TID model.
  • Demonstrated strength, reliability, and efficacy through negligible absolute errors.
  • Achieved high accuracy indicated by mean squared errors and optimal modeling index for regression.

Conclusions:

  • The NNLMA is a robust and effective computational tool for analyzing non-linear TID models.
  • The study validates the use of soft computing approaches in understanding complex biological dynamics.
  • Results support the potential of NNLMA for future research in tumor immunology and computational modeling.