DNA Clustering Algorithms

Stepanyan, Ivan V.

doi:10.3103/s0005105521010039

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allows us to visualize and compare diverse types of coronaviruses in their easy-to-perceive parametric space. Our earlier articles [16][17][18][19][20] describe in detail all methods of constructing visualizations.…”

Section: Discussionmentioning

confidence: 99%

“…The binary sub-alphabets in the set of nitrogenous bases differ according to the kinds of boolean properties: C = T "pyrimidines"/A = G "purines"; A = C "amino"/G = T "keto"; C = G has 3 hydrogen bonds and A = T has two hydrogen bonds. Based on the boolean properties method for scale-parametric analysis of large nucleotide sequences described in [16][17][18][19][20] is as follows:…”

Section: Methods: An Algorithm For Genometric Visualization Of Geneti...mentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Different Strains of Coronavirus Based on Genometric Mappings

Stepanyan

Lednev

2022

Symmetry

View full text Add to dashboard Cite

Coronaviruses are viruses that infect the respiratory system of humans. Besides high mortality rates among the population, they brought about several economic crises on a global scale. Methods. To study and identify features in the genetic composition of the nucleotide sequences of various coronaviruses, we applied copyright algorithms and visualization, which allowed us to compare the biochemical parameters of diverse RNA coronaviruses in a visual form. Results. The article provides examples of different approaches to imaging coronaviruses. We have provided examples of coronavirus RNA structure visualization in various parametric spaces (1-D and 2-D). We employed various visualization types, including structural, integral, and frequency. The research discussed methods of visualization. Our team developed visualization and comparative analysis of coronavirus serotypes and visualization of SARS-CoV-2 coronavirus datasets. Discussion followed on the visualization results. The presented techniques and the results allowed for displaying the structure of RNA sequences of coronaviruses in spaces of various dimensions. Conclusions. According to our findings, the proposed method contributes to the visualization of the genetic coding of coronaviruses. We discussed the issues of machine learning and neural network technology concerning the analysis of coronaviruses based on the presented approach. The described line of research is essential for the study and control of complex quantum mechanical systems, such as RNA or DNA.

show abstract