An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
Keyword(s):
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 with the increase of genomic data. For that reason, alignment-free 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; finally the last one, process 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.
2001 ◽
Vol 300
(1-2)
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pp. 271-284
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Keyword(s):
2010 ◽
Vol 19
(1)
◽
pp. 010205-8
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Keyword(s):
2019 ◽
Vol 26
(2)
◽
pp. 143-151
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Keyword(s):