Tsallis conditional mutual information in investigating long range correlation in symbol sequences

Papapetrou, Maria; Kugiumtzis, Dimitris

doi:10.1016/j.physa.2019.123016

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…Recently, the Tsallis mutual information came into the focus for studying long range correlations in symbol sequences [70]. It is related to the Tsallis α-entropy…”

Section: Tsallis α-Entropy and Related Mutual Information Functionsmentioning

confidence: 99%

“…These mutual information concepts can be used to generate information theoretic features for sequence analysis: Rényi entropic profiles were considered for DNA classification problems based on chaos game representation [67,68]. Molecular descriptors based on the Rényi entropy were investigated in [69], whereas long range correlation using Tsallis mutual information was considered in [70]. However, to our best knowledge, MIF for these variants are not known so far.…”

Section: Introductionmentioning

confidence: 99%

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

Bohnsack

Kaden

Abel

et al. 2021

Entropy

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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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“…Recently, the Tsallis mutual information came into the focus for studying long range correlations in symbol sequences [70]. It is related to the Tsallis α-entropy…”

Section: Tsallis α-Entropy and Related Mutual Information Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bohnsack

Kaden

Abel

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…In information theory, the mutual information (MI) is a measure of the mutual dependence between the two time series [35,36]. Specifically, MI quantifies the random dependency between two stochastic variables without making any hypothesis about the nature of the relationship [37].…”

Section: Mutual Informationmentioning

confidence: 99%

Quantifying the Cross-Correlations between Online Market Participation Willingness and Stock Market Dynamics

Chu

Zhang

2020

Complexity

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The investors’ market participation willingness plays a vital role in the decision-making process of asset allocation. With the newly emerged dataset of investors’ market participation willingness, this paper provides the first evidence on the dynamic relationship between market participation willingness and the market dynamics in the Chinese stock market. We select four typical Chinese stock market indices, i.e., SSE50 Index, CSI300 Index, Small and Medium Enterprise Market Index, and Growth Enterprise Market Index, to represent different aspects of the Chinese stock market. Moreover, we use mutual information to measure the overall dependence between market participation willingness and stock market and employ the DCCA cross-correlation coefficient and MF-DCCA to investigate the cross-correlation between market participation willingness and market dynamics. We find that there exist overall dependence and power-law cross-correlation between market participation willingness and the Chinese stock market, and the cross-correlations are significantly multifractal.

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“…More specifically, Melnik and Usatenko (2014) , using an additive Markov chain approach, analyzed DNA molecules of different organisms, and they estimated the differential entropy for the biological classification of these organisms. Similarly, Papapetrou and Kugiumtzis (2014 , 2020) studied DNA sequences, through the estimation of the Markov chain orders and Tsallis conditional mutual information. The results showed a different long memory structure in their DNA samples (coding and non-coding).…”

Section: Introductionmentioning

confidence: 99%

Spatial constrains and information content of sub-genomic regions of the human genome

et al. 2021

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Summary Complexity metrics and machine learning (ML) models have been utilized to analyze the lengths of segmental genomic entities of DNA sequences (exonic, intronic, intergenic, repeat, unique) with the purpose to ask questions regarding the segmental organization of the human genome within the size distribution of these sequences. For this we developed an integrated methodology that is based upon the reconstructed phase space theorem, the non-extensive statistical theory of Tsallis, ML techniques, and a technical index, integrating the generated information, which we introduce and named complexity factor (COFA). Our analysis revealed that the size distribution of the genomic regions within chromosomes are not random but follow patterns with characteristic features that have been seen through its complexity character, and it is part of the dynamics of the whole genome. Finally, this picture of dynamics in DNA is recognized using ML tools for clustering, classification, and prediction with high accuracy.

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Tsallis conditional mutual information in investigating long range correlation in symbol sequences

Cited by 4 publications

References 46 publications

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

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

Quantifying the Cross-Correlations between Online Market Participation Willingness and Stock Market Dynamics

Spatial constrains and information content of sub-genomic regions of the human genome

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