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The classification of DNA sequences is a key research area in bioinformaticsas it enables researchers to conduct genomic analysis and detect possiblediseases. In this paper, three state-of-the-art algorithms, namelyConvolutional Neural Networks, Deep Neural Networks, and N-gram ProbabilisticModels, are used for the task of DNA classification. Furthermore, we introducea novel feature extraction method based on the Levenshtein distance andrandomly generated DNA sub-sequences to compute information-rich features fromthe DNA sequences. We also use an existing feature extraction method based on3-grams to represent amino acids and combine both feature extraction methodswith a multitude of machine learning algorithms. Four different data sets, eachconcerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C,are used for evaluating the different approaches. The results of theexperiments show that all methods obtain high accuracies on the different DNAdatasets. Furthermore, the domain-specific 3-gram feature extraction methodleads in general to the best results in the experiments, while the newlyproposed technique outperforms all other methods on the smallest Covid-19dataset

Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification