The discipline of computational biology faces a difficult research problem in DNA sequence reconstruction. It is impossible to sum up the evolution of the DNA in a few simple factors. As a result, a modelling strategy is required for examining DNA patterns. In this publication, we suggested a brand-new framework for studying DNA pattern. The suggested framework is divided into two phases. While the other step is used for the
reconstruction process, the first stage is used to analyse the evolution of DNA sequences. For the purpose of studying and forecasting the DNA sequence, we used cellular automata rules. The reconstruction technique is then updated, and the Particle Swarm Optimization algorithm and Probabilistic Cellular Automata are added into it. The integrated system increases the suggested framework's effectiveness and achieves the best transitional guidelines. The premise of our novel approach is that mutations are probabilistic occurrences. As a result, a PCA model can be used to simulate their progression.
KeywordsDNA sequence reconstruction; Computational biology; Mutation rates; Probabilistic cellular automata (PCA); Particle swarm optimization (PSO)
Published Date: 2023-02-28; Received Date: 2023-02-02
