  The classification of DNA sequences is a key research area in bioinformatics
as it enables researchers to conduct genomic analysis and detect possible
diseases. In this paper, three state-of-the-art algorithms, namely
Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic
Models, are used for the task of DNA classification. Furthermore, we introduce
a novel feature extraction method based on the Levenshtein distance and
randomly generated DNA sub-sequences to compute information-rich features from
the DNA sequences. We also use an existing feature extraction method based on
3-grams to represent amino acids and combine both feature extraction methods
with a multitude of machine learning algorithms. Four different data sets, each
concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C,
are used for evaluating the different approaches. The results of the
experiments show that all methods obtain high accuracies on the different DNA
datasets. Furthermore, the domain-specific 3-gram feature extraction method
leads in general to the best results in the experiments, while the newly
proposed technique outperforms all other methods on the smallest Covid-19
dataset
