基于特征提取的机器学习算法在DNA分类中的比较
Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification
摘要：
DNA序列的分类是生物信息学的一个关键研究领域，因为它使研究人员能够进行基因组分析和检测可能的疾病。在本文中，三个国家的最先进的算法，即卷积神经网络，深度神经网络，和N-gram概率模型，用于DNA分类的任务。此外，我们介绍了一种新的特征提取方法的基础上的Levenshtein距离和随机生成的DNA子序列计算信息丰富的功能，从DNA序列。我们还使用现有的特征提取方法的基础上3-gram代表氨基酸，并结合这两种特征提取方法与众多的机器学习算法。四个不同的数据集，每个数据集都涉及病毒性疾病，如COVID-19，艾滋病，流感和丙型肝炎，用于评估不同的方法。实验结果表明，所有方法在不同的DNA数据集上都获得了较高的准确率。此外，特定领域的3-gram特征提取方法通常在实验中获得最佳结果，而新提出的技术在最小的Covid-19数据集上优于所有其他方法
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
