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Special Issue “Coexistence of Complexity Metrics and Machine-Learning Approaches for Understanding Complex Biological Phenomena”

A special issue of Entropy (ISSN 1099-4300).

Special Issue link

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Dimitrios S. Monos Website
Guest Editor
Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
Interests: functional–structural aspects of histocompatibility molecules

Dr. Leonidas P. Karakatsanis Website
Guest Editor
Department of Environmental Engineering, Democritus University of Thrace, Xanthi 671 00, Greece
Interests: complexity; nonlinear systems; Tsallis non-extensive statistics; machine learning; coding DNA; non-coding DNA; biological complexity; complexity metrics; phase space

Special Issue Information

Dear Colleagues,

The dynamics of complex systems and the ways in which they influence a number of biological processes are one of the most interesting physical problems through which current developments in the independent fields of physics and biology/genomics can be brought together and that they can attempt to address more effectively. These dynamics include the hierarchy of complex and self-organized phenomena such as intermittent turbulence, fractal structures, long-range correlations, far-from-equilibrium phase transitions, anomalous diffusion–dissipation and strange kinetics, the reduction of dimensionality in phase space etc. At equilibrium, the dynamical attractive phase space is practically infinitely dimensional, as the system state evolves in all dimensions according to the famous ergodic theorem of Boltzmann–Gibbs statistics. Far from equilibrium, the statistics of the dynamics follow the q-Gaussian generalization of the B–G statistics or other more generalized statistics. In Tsallis q-statistics, even for the case of q = 1 (corresponding to the Gaussian process), the non-extensive character permits the development of long-range correlations produced by equilibrium phase-transition multi-scale processes.

Many scientists have used complexity metrics such as generalized entropies, multifractal analysis, q-triplet of Tsallis statistics, complex networks, fractal dimension etc. to understand the complex behaviour of complex phenomena in biology/genomics. The projection of the dynamics to the statistics in the phase space develops a complete picture that can be integrated to the variations of the complexity metrics. This picture of dynamics can be identified from machine-learning tools for clustering, classification and prediction. The merging of complexity theory and machine-learning approaches can provide semantic results enabling a deeper understanding and promotion of the fundamental laws of complex biological phenomena.

This Special Issue emphasizes the merging of the complexity metrics and the machine-learning approaches, hoping to attain a deeper understanding of complex biological phenomena. The analysis and study of complex biological phenomena based on the aforementioned statistical approaches fall within the scope of this Special Issue.

Prof. Dr. Dimitrios S. Monos
Dr. Leonidas P. Karakatsanis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.


  • complexity metrics
  • generalized entropies
  • Tsallis q-triplet
  • Tsallis entropy
  • machine learning
  • phase space
  • biological complexity
  • coding DNA
  • non-coding DNA
  • genomics
  • evolutional biology

Published Papers

This special issue is now open for submission.