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A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth.

Helena Coggan1, Helena Andres Terre2, Pietro Liò2

  • 1Department of Mathematics, University College London, London, United Kingdom.

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This study introduces a novel physics-inspired algorithm for modeling cancer progression, offering faster and more reliable patient-specific predictions than traditional machine learning methods.

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Area of Science:

  • Computational biology
  • Mathematical modeling
  • Cancer research

Background:

  • Machine learning (ML) is increasingly used for analyzing biological systems, including cancer progression.
  • A key limitation of ML models is their inherent complexity and nonlinearity, hindering deterministic input-output relationships and accountability.
  • Existing ML approaches often lack transparency in predicting cancer growth dynamics.

Purpose of the Study:

  • To develop a physics-inspired computational model for tumor growth analysis.
  • To create an algorithm that overcomes the limitations of traditional machine learning in cancer modeling.
  • To enhance the speed and reliability of identifying patient-specific cancer growth models.

Main Methods:

  • Developed a physics-inspired model for tumor growth, avoiding stochastic gradient descent and artificial nonlinearities.
  • Derived a novel algorithm for exploring parameter space in tumor growth models.
  • Compared the algorithm's performance against simulated annealing for identifying model parameters.

Main Results:

  • The new algorithm identifies candidate equations for tumor growth significantly faster than simulated annealing.
  • Testing on synthetic data demonstrated the algorithm's efficiency in narrowing down the correct parameter space.
  • The approach proved reliable in locating parameters for tumor growth models.

Conclusions:

  • The proposed physics-inspired algorithm offers a more accountable and efficient alternative to current machine learning methods for cancer progression analysis.
  • This approach has the potential to significantly improve the speed and reliability of developing patient-specific cancer growth models in clinical settings.
  • Enhanced computational methods can accelerate personalized medicine in oncology.