Computational Biology Approaches to Genome and Protein Analyzes, 2nd Edition

Dear Colleagues,

This is the second volume of the previously successful Special Issue "Computational Biology Approaches to Genome and Protein Analyzes".

The aim of this Special Issue is to collect research articles on, among other topics, methods (including artificial intelligence, machine learning, mathematical, statistical methods), algorithms, and their implementations in the field of computational biology.

Currently, the amount of experimental biological data is continuing to increase. This is connected with the necessity of developing new more effective methods, algorithms, and implementations as computer programs, which will allow for an in-depth understanding of the meaning of the data. New approaches are especially important during the analysis of genomes (including sequences) and proteins, allowing for the establishment of a complex sequence–function relationship. These approaches can rely on the "computing power" needed, like when generating phylogenetic trees using ever faster computers. New approaches can also use artificial intelligence methods, especially artificial neural networks, fuzzy logic, and expert systems. Using artificial neural networks, for example, makes it possible to replace computation with recognition. Moreover, regardless of the method used, the interpretation of the results always plays a key role, and in this area, computer methods can also play an important role by effectively supporting scientists. New approaches are particularly important when analyzing genome changes during the evolution of normal organisms and the development of transformed cells, as these areas are relatively poorly understood.

Potential topics covered by this Special Issue include, but are not limited to, ideas, methods (including artificial intelligence, machine learning, mathematical, and statistical methods), algorithms, implementations, and computer programs used to analyze genomes (including sequences) and proteins, as well as establishing a sequence–function relationship. Original articles and reviews addressing these topics are welcomed.

Prof. Dr. Andrzej Kasperski
Guest Editor

Manuscript Submission Information

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• artificial neural networks
• artificial intelligence methods
• cancer development analysis
• cell-fate and genome attractors
• computational biology
• computerized analysis of genomes (including sequences) and proteins
• computerized identification and recognition of evolution
• dot-matrix method
• genetic semihomology
• expert systems
• evolutionary distances and trees
• machine learning
• multiple alignment
• navigating cancer network attractors
• neural networks
• pattern recognition
• phylogenetic trees
• sequence–function relationship
• sequence similarity
• Protein contact networks (PCNs)