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Related Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques.

Andrea Murari1, Riccardo Rossi2, Michele Lungaroni2

  • 1Consorzio RFX (CNR, ENEA, INFN, Universita' di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova, Italy.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

Neural networks offer superior correlation analysis for complex systems. These advanced tools accurately estimate total influence, including direction, outperforming traditional methods in accuracy and efficiency.

Keywords:
autoencodersencodersinformation quality ratioinformation theorymachine learning toolstotal correlations

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Area of Science:

  • Complex Systems Analysis
  • Computational Statistics
  • Machine Learning Applications

Background:

  • Traditional correlation coefficients like Pearson's are limited to linear relationships.
  • Mutual information and information quality ratio capture nonlinear effects but lack normalization and sign information.
  • Accurate probability distribution estimation is often hindered by data limitations.

Purpose of the Study:

  • To develop and evaluate neural computational tools for robust correlation estimation in complex systems.
  • To assess the capability of these tools in determining total influence, including the sign of the relationship.
  • To compare the performance of neural methods against traditional correlation indicators.

Main Methods:

  • Development of specific neural network architectures, including encoders and autoencoders.
  • Utilizing synthetic data for extensive numerical testing and validation.
  • Analysis of accuracy, computational efficiency, and robustness against noise.

Main Results:

  • Neural computational tools demonstrated superior accuracy in estimating total correlation compared to traditional methods.
  • These tools successfully determined the sign of the mutual influence between quantities.
  • The developed neural networks showed high computational efficiency and robustness against noise.

Conclusions:

  • Neural computational tools provide a more comprehensive and robust approach to correlation analysis in complex systems.
  • These methods overcome limitations of traditional indicators, offering sign information and improved accuracy.
  • Neural networks represent a significant advancement for analyzing complex system interdependencies.