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The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable

Katrin Sophie Bohnsack1, Marika Kaden1, Julia Abel1

  • 1Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.

Entropy (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

We introduce mutual information functions as biomolecular sequence fingerprints for robust classification. This method enhances understanding of sequence data through interpretable machine learning models.

Keywords:
classificationinterpretable modelsmachine learningmutual informationsequence analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Biomolecular sequence analysis often relies on complex feature extraction.
  • Existing methods may lack interpretability, hindering deeper biological insights.
  • Robust classification is crucial for understanding sequence function and evolution.

Purpose of the Study:

  • To apply mutual information variants as novel fingerprints for biomolecular sequence classification.
  • To integrate these fingerprints with interpretable machine learning models for enhanced knowledge extraction.
  • To demonstrate the utility of this approach across diverse biomolecular sequence datasets.

Main Methods:

  • Utilizing resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy.
  • Employing generalized learning vector quantization (GLVQ) as an interpretable classifier.
  • Validating the methodology on various biomolecular sequence datasets.

Main Results:

  • Achieved high classification ability for biomolecular sequences using mutual information fingerprints.
  • Demonstrated substantial knowledge extraction through interpretable GLVQ models.
  • Showcased the robustness and versatility of the proposed methodology.

Conclusions:

  • Mutual information functions provide effective fingerprints for biomolecular sequence classification.
  • Interpretable machine learning models offer valuable insights beyond classification accuracy.
  • The proposed approach enhances both the predictive power and understanding of sequence data analysis.