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Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis
Robson P Bonidia1, Anderson P Avila Santos1,2, Breno L S de Almeida1
1Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil.
Entropy (Basel, Switzerland)
|July 8, 2023
Summary
This study introduces a new Tsallis entropy feature extractor for biological sequence analysis. It outperforms Shannon entropy and aids in reducing data dimensions for machine learning classification.
Area of Science:
- Bioinformatics
- Computational Biology
- Information Theory
Background:
- Accelerated advances in sequencing technology have led to massive biological data, creating challenges for sequence analysis.
- Machine learning (ML) algorithms are increasingly used for biological sequence classification, but require effective feature extraction methods.
- Information Theory concepts, like Shannon and Tsallis entropy, offer statistical frameworks for analyzing sequence data.
Purpose of the Study:
- To propose and evaluate a novel Tsallis entropy-based feature extractor for biological sequence classification.
- To assess the effectiveness of Tsallis entropy in capturing relevant biological sequence information.
- To investigate the utility of Tsallis entropy for dimensionality reduction in biological data.
Main Methods:
- Development of a Tsallis entropy-based feature extractor.
- Comparative analysis against Shannon entropy and other generalized entropies.
- Evaluation through five case studies, including analysis of the entropic index q and performance on diverse datasets.
- Investigation of Tsallis entropy's role in dimensionality reduction techniques.
Main Results:
- The proposed Tsallis entropy feature extractor demonstrated superior performance compared to Shannon entropy for biological sequence classification.
- The method showed robustness in generalization across different datasets.
- Tsallis entropy proved effective for information collection in reduced dimensions, outperforming methods like SVD and UMAP.
Conclusions:
- The novel Tsallis entropy-based feature extractor is a powerful tool for biological sequence analysis and classification.
- This approach offers an effective alternative to traditional methods, particularly in handling large-scale biological data.
- Tsallis entropy provides a promising avenue for dimensionality reduction in bioinformatics, enhancing ML model efficiency.
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