Coexistence of Complexity Metrics and Machine-Learning Approaches for Understanding Complex Biological Phenomena

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.

Dr. Leonidas P. Karakatsanis

Prof. Dr. Dimitrios S. Monos
Guest Editors

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