A Comparative Study of Protein Sequences Classification-Based Machine Learning Methods for COVID-19 Virus against HIV-1

The effective spread of COVID-19 cases in several countries produces more protein sequences that are released in genomic public sources. It provides some awareness and indications for virus classification of COVID-19 and HIV-1 that are essential for drug discovery of COVID-19. This paper reveals the importance of machine learning algorithms to handle the recognition of two different viruses. Therefore, 18,476 protein sequences for both COVID-19 and HIV-1 and 9238 for each virus are applied to the proposed model based on feature extraction, data labeling, and six classifiers. Amino acid classification according to their dipoles and volumes is employed as a feature extraction tool based on the creation of eight features from twenty amino acids by using the conjoint triad (CT) method. The data labeling is employed as a coding tool by binary numbers refereeing zero for COVID-19 and one for HIV-1. The random forest (RF) model achieved the highest classification accuracy of 99.89% for eight features and 97.80% for two features. The experimental results significantly confirmed that eight features required more computational time than two features, but the accuracy rate was nearly similar in the two cases. This classification strategy of COVID-19 and HIV-1 will prompt the prediction of protein sequences of the new virus.