Abstract
Algorithms for scaling and visualization of nucleotide sequences developed in this study allow identifying relationships between the biochemical parameters of DNA and RNA molecules with scale invariance, fractal clusters, nonlinear ordering and symmetry and noise immunity of visual representations in orthogonal coordinate systems. The algorithms are capable of displaying structures of the nucleotide sequences of living organisms by visualizing them in spaces of various dimensions and scales. Approximately one hundred genes (protozoa, plants, fungi, animals, viruses) were analysed and examples of visualization of the nucleotide composition of genomes of various species have been presented. The developed method contributes to an in-depth understanding of the principles of genetic coding and simplifying the perception of genetic information due to the algorithmic interpretation of the basic properties of polynucleotide fragments with visualization of the final geometric structure of the genetic code.
Highlights
Mathematical biology is based on computational methods and algorithms that allow to acquire scientific knowledge, model biological processes and phenomena
The same applies to the task of analyzing the variability of the physicochemical parameters of long nucleotide sequences that occur in the form of DNA and RNA molecules
To analyze complex multi-parameter phenomena, which include the phenomenon of genetic coding, methods of lowering dimensions are used, including machine learning
Summary
Mathematical biology is based on computational methods and algorithms that allow to acquire scientific knowledge, model biological processes and phenomena. There is the problem of perceiving complex biological information, including phenomena that occur inside the cell. This problem relates to the psychophysiology of perception of any multidimensional information. It is rather difficult to imagine all processes occurring in a living cell, despite the strong theoretical foundation and the presence of a well-developed mathematical apparatus. To analyze complex multi-parameter phenomena, which include the phenomenon of genetic coding, methods of lowering dimensions are used, including machine learning (neural network clustering, classification, deep learning, etc.)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
