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Data-driven learning of Boolean networks and functions by optimal causation entropy principle
Jie Sun1,2, Abd AlRahman R AlMomani3, Erik Bollt2,4
1Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA.
Patterns (New York, N.Y.)
|November 24, 2022
Summary
We introduce Boolean optimal causation entropy, a new information theoretic method for learning Boolean networks from data. This approach is efficient, noise-resilient, and effective for feature selection in complex systems.
Area of Science:
- Computational biology
- Bioinformatics
- Systems biology
Background:
- Boolean networks model complex systems in biology, finance, and decision-making.
- Automated learning of these networks is challenging due to unknown structures and functions.
Purpose of the Study:
- Develop a computationally efficient and noise-resilient information theoretic methodology for learning Boolean networks.
- Enable effective feature selection for identifying key drivers in complex systems.
Main Methods:
- Introduced Boolean optimal causation entropy (BOCE), an information theoretic approach.
- Demonstrated computational efficiency and resilience to noise.
- Applied BOCE for feature selection in networked Boolean function reduced-order models.
Main Results:
- BOCE significantly outperforms previous methods in learning Boolean networks.
- The method effectively identifies informative features for complex system analysis.
- Successful application in diverse real-world examples including medical diagnosis and financial risk analysis.
Conclusions:
- Boolean optimal causation entropy offers a powerful and efficient tool for analyzing complex data systems.
- The methodology facilitates robust feature selection, enhancing model interpretability.
- This approach has broad applicability across scientific and financial domains.
Keywords:
Boolean functionBoolean networkcausal network inferenceentropyinformation flowquantitative biologyrisk analysisMore Related Videos
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