ArticleEntropy (Basel, Switzerland)2021
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A new SNP comparison (SnpC) method for detecting unique/enriched gene-SNP variants and comparing population gene mutation diversity.BMC bioinformatics · 2026Article
- [Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
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5 authors.
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Abstract
In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.
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