Combining a genetic algorithm and ensemble method to improve the classification of viruses

Genomic features identification is an important step toward machine learning (ML) derived classifiers. Designing robust feature identification methods can increase the performance of ML algorithms and reduce high dimensional data. The Classification of genomes from nucleotides sequences often requires an exponential feature space according to the size of the genomes. These feature spaces should also capture the abundant mutations affecting the genomic sequences over time of several complex viruses such as the human immunodeficiency virus (HIV). One way of overcoming such challenges could be to design an accurate and efficient algorithm to capture a bag of minimal subsets of genomic features based on k-mers that could represent the most likely alternative and evolutionary space. In this article, we introduce KEVOLVE, a new method based on a Genetic Algorithm (GA) including a ML kernel to extract a bag of minimal subsets of genomic features maximizing a given classification score threshold. K EVOLVE is coupled with an ensemble prediction model based on support vector machines (SVMs) and is applied on the classification of HIV genomic sequences. Subsets of genomic features identified reduce the size of initial feature matrices by 99% and models based on them outperform state-of-the-art HIV predictors. The results also show that KEVOLVE is less sensitive to high mutation rates of the virus. The source code is available at https://github.com/bioinfoUQAM/Kevolve