Classification of Retroviruses Based on Genomic Data Using RVGC

Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnol-ogy Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on some significant results, the number of features used for classification is reduced to five. The classification shall be carried out in two phases. The first phase of classification shall select only two features. Unclassified data in the first phase is transferred to the next phase, where the final decision is taken with the remaining three features. Three data sets of animals and human retroviruses are selected; the training data set is used to minimize the classifier's number and training; the validation data set is used to validate the models. The performance of the classifier is analyzed using the test data set. Also, we use decision tree, naive Bayes, k-nearest neighbors, and vector support machines to compare results. The results show that the proposed approach performs better than the existing methods for the retrovirus's imbalanced genome-sequence dataset.