Bio-Inspired Computation Based on Mathematical Modeling—Theories and Applications in Health Sciences

Dear Colleagues,

It is estimated that biological life that has evolved over millions of years will be a fruitful source of inspiration for the development of novel computational mathematical methods in the years to come, representing important research directions on mathematical modeling in the field of artificial intelligence.

Diverse problems in health sciences are computationally hard and cannot be solved efficiently via actual methods. Mathematical modeling based on bio-inspired computing applied to solve computationally hard problems in health sciences domains is an emerging field in this area. Bio-inspired computing has been proven successful for solving a large variety of problems in domains of science where other methods have not been so effective.

In this context, this Special Issue invites researchers to contribute original works regarding the development of bio-inspired computational and hybrid methods for solving difficult problems in health sciences that represent significant scientific advancements.

Recommended topics include (but are not limited to) the following:

• Mathematical optimization;
• Mathematical modeling;
• Artificial intelligence;
• Natural computing;
• Agents and multi‐agent systems;
• Complex networks;
• Decision support systems;
• Bioinformatics;
• Biomathematics;
• Sequence analysis;
• DNA sequencing;
• Sequence assembly;
• Gene function prediction;
• Computational evolutionary biology;
• Comparative genomics;
• Genetics of disease;
• Analysis of mutations in cancer;
• Gene and protein expression;
• Analysis of regulation;
• Analysis of the cellular organization;
• Structural bioinformatics;
• Network and systems biology;
• Molecular interaction network.

Prof. Dr. Laszlo Barna Iantovics
Prof. Dr. Matthias Dehmer
Prof. Dr. Francesco Flammini
Prof. Dr. Frank Emmert-Streib
Guest Editors

Manuscript Submission Information

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• mathematical optimization
• mathematical modeling
• artificial intelligence
• natural computing
• agents and multi‐agent systems
• complex networks
• decision support systems
• bioinformatics
• biomathematics
• sequence analysis
• DNA sequencing
• sequence assembly
• gene function prediction
• computational evolutionary biology
• comparative genomics
• genetics of disease
• analysis of mutations in cancer
• gene and protein expression
• analysis of regulation
• analysis of the cellular organization
• structural bioinformatics
• network and systems biology
• molecular interaction network