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Information Theory Based Methods in Machine Learning and Bioinformatics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 9687

Special Issue Editors


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Guest Editor
Saxon Institute for Computational intelligence and Machine Learning, University of Applied Sciences Mittweida (UASM), 09648 Mittweida, Germany
Interests: machine learning; computational intelligence; interpretable models; information theory; pattern recognition; learning vector quantization; bioinformatics

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Guest Editor
School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: theory and interdisciplinary applications of machine learning; probabilistic modeling and dynamical systems

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Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: machine learning for signal processing; information theoretic learning; representation learning; computer vision; computational neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information theoretic methods constitute a universal principle in science. In machine learning, such principles stimulate new concepts and can justify existing heuristic approaches, as well as giving new insights into established theories. Many machine learning problems, such as clustering, classification, dimensionality reduction and metric learning, have been framed in the context of information theory. In particular, in bioinformatics, information theoretic approaches in machine learning provide principles and paradigms for efficient information extraction and processing, which constitute modern statistical tools for advanced genomics, proteomics, structure analytics, etc.

The aim of this Special Issue is to collect recent results on information theory-related machine learning methods in bioinformatics. We also invite submissions about new perspectives, currently ongoing research, and discussions regarding existing approaches. As such, the papers can either provide theoretical perspectives, highlight outstanding applications, or introduce new perspectives and concepts in bioinformatics. Review papers dedicated to specific aspects of information theoretic learning in biomedical contexts are also welcome.

In the filed of bioinformatics, we emphasize topics such as sequence analysis using information theoretic machine learning methods, applications in molecular biology, structure analysis, as well as applications in biomedicine. These topics are not exclusive; papers addressing other bioinformatic topics related to information theoretic methods in machine learning will be considered as well.

Prof. Dr. Thomas Villmann
Prof. Dr. Peter Tino
Dr. Luis Gonzalo Sánchez Giraldo
Guest Editors

Manuscript Submission Information

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Published Papers (2 papers)

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21 pages, 15624 KiB  
Article
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers
by Katrin Sophie Bohnsack, Marika Kaden, Julia Abel, Sascha Saralajew and Thomas Villmann
Entropy 2021, 23(10), 1357; https://doi.org/10.3390/e23101357 - 17 Oct 2021
Cited by 4 | Viewed by 2744
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 [...] Read more.
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. Full article
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30 pages, 1533 KiB  
Article
Solving Schrödinger Bridges via Maximum Likelihood
by Francisco Vargas, Pierre Thodoroff, Austen Lamacraft and Neil Lawrence
Entropy 2021, 23(9), 1134; https://doi.org/10.3390/e23091134 - 31 Aug 2021
Cited by 48 | Viewed by 5886 | Correction
Abstract
The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and [...] Read more.
The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments. Full article
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