An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification

V. M. Arceda
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引用次数: 1

Abstract

Viral subtyping classification is very relevant for the appropriate diagnosis and treatment of illnesses. The most used tools are based on alignment-based methods, nevertheless, they are becoming too slow due to the increase of genomic data; for that reason, alignmentfree methods have emerged as an alternative. In this work, we analyzed four alignment-free algorithms: two methods use k-mer frequencies (Kameris and Castor-KRFE); the third method used a frequency chaos game representation of a DNA with CNNs; and the last one processes DNA sequences as a digital signal (ML-DSP). From the comparison, Kameris and Castor-KRFE outperformed the rest, followed by the method based on CNNs.
基于SVM和CNN的k-mer频率特征的病毒亚型分类分析
病毒亚型分类对疾病的正确诊断和治疗具有重要意义。最常用的工具是基于比对的方法,然而,由于基因组数据的增加,它们变得太慢了;出于这个原因,不需要对齐的方法已经成为一种替代方法。在这项工作中,我们分析了四种无对准算法:两种方法使用k-mer频率(Kameris和Castor-KRFE);第三种方法是用cnn对DNA进行频率混沌博弈表示;最后一个是将DNA序列作为数字信号处理(ML-DSP)。从比较来看,Kameris和Castor-KRFE方法表现最好,其次是基于cnn的方法。
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