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Updated: Dec 19, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Disease evolution in reaction networks: Implications for a diagnostic problem.

Abolfazl Ramezanpour1,2, Alireza Mashaghi1

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Summary
This summary is machine-generated.

We developed a model to track disease progression by analyzing symptom dynamics in reaction networks. This approach helps determine optimal medical intervention times based on disease phenotype and symptom evolution.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Disease progression involves complex changes in biological systems.
  • Understanding symptom dynamics is crucial for accurate diagnosis and treatment.
  • Stochastic modeling offers a framework to study biological system evolution.

Purpose of the Study:

  • To model the time evolution of disease phenotypes using reaction networks.
  • To investigate how network defects impact symptom probability distributions.
  • To establish a framework for dynamical, history-based disease diagnosis.

Main Methods:

  • Utilized a large population of reaction networks with stochastic dynamics.
  • Defined healthy and disease states using an objective function (mutual information).
  • Employed a reverse simulated annealing algorithm to model disease emergence.

Main Results:

  • The model quantifies the time-dependent probability of signs for a given disease phenotype.
  • Diagnosis accuracy was assessed as a function of time.
  • Identified a trade-off between diagnostic accuracy and objective function over time.

Conclusions:

  • The developed model provides insights into disease progression and symptom dynamics.
  • It enables the estimation of disease likelihood based on temporal sign evolution.
  • The findings suggest optimal timing for medical interventions based on diagnostic accuracy and disease state.