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

• URL: http://arxiv.org/abs/2011.00485v1
• Date: Sun, 1 Nov 2020 12:04:54 GMT
• Title: Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification
• Authors: Xiangxie Zhang, Ben Beinke, Berlian Al Kindhi and Marco Wiering
• Abstract summary: 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. We introduce a novel feature extraction method based on the Levenshtein distance and randomly generated DNA sub-sequences. Four different data sets, each concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C, are used for evaluating the different approaches.
• Score: 0.7742297876120561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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