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

Entropy

Sascha Saralajew¹  Thomas Villmann²  Katrin Sophie Bohnsack²  Marika Kaden²  Julia Abel² 

[1] Bosch Center for Artificial Intelligence, 71272 Renningen, Germany;Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany;

关键词: mutual information;    sequence analysis;    classification;    machine learning;    interpretable models;   

DOI  :  10.3390/e23101357

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown