Genetic Algorithm Approach to Find the Estimated Value of HMM Parameters for NS5  Methyltransferase Protein

Abstract

Dengue is the pandemic disease caused by Dengue virus (DENV), a mosquito-borne flavivirus. In recent years dengue has emerged as a foremost cause of severe illness and deaths in developing countries. About 400 million dengue infections occur worldwide each year. In general, dengue infections create only mild illness but infrequently expand into a lethal illness termed as severe dengue for which no specific treatment. The machine learning approach plays a significant role in bioinformatics and other fields of computer science. It exploits approaches like Hidden Markov Model (HMM), Genetic Algorithm (GA), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The GA is a randomized search algorithm for solving the problem based on natural selection phenomena. Many machine learning techniques are based on HMM have been positively applied. In this work, We firstly used HMM parameters on the biological sequence, and after that, we catch the probability of the observation sequence of a mutated gene sequence. This study compares both methods, G.A. and HMM, to get the highest estimated value of the observation sequence. In this paper, we also discuss the applications of GA in the bioinformatics field. In a further study, we will apply the other machine learning approaches to find the best result of protein studies. Funding Information: This work did not provide any source of financial support. Declaration of Interests: All the authors declare that there are no conflicts of interest regarding the publication of this research paper.