TECHNIQUE FOR THE ANALYSIS OF BIOINFORMATION DATA OF GENOMIC NATURE FOR THE DEVELOPMENT OF MULTIEPITOPE ANTICORONAVIRUS VACCINE MODELS

Abstract

Background. The coronavirus epidemic continues, but there is evidence that the society has already mastered effective measures for the prevention and treatment of this disease. The unresolved problems are prevention, early diagnosis and timely treatment of new viral epidemics, prevention and treatment of postcovid complications, the mortality from which manifests itself latently under the guise of other diseases and does not fall into the statistics of the coronavirus pandemic. Materials and methods. Based on the developed original data processing technique, designed for analyzing coronavirus genomic data, models of the anti-coronavirus multi-epitope vaccine were computed and tested in silico. In a series of computational experiments, evidence of their possible efficiency and safety was obtained. Results and conclusions. Based on research experiments and analysis of scientific literature, recommendations are formulated for the development and application of epitope antiviral vaccines using the example of the anti-coronavirus vaccine.

Keywords

• medical systems
• information representation
• software
• algorithms
• databases
• genomics
• transcriptomics
• machine learning
• artificial intelligence
• programming languages
• applied mathematics
• biophysics
• data science
• data mining
• coronavirus
• epidemic
• pandemic
• bioinformatics
• immunoinformatics
• antiviral therapy
• vaccines
• epitopes