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

Machaca Arceda V.E. · Journal of Computer Science and Technology (Argentina) (2020) · DOI: 10.24215/16666038.20.e11

Resumen

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, 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; 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.

Instituciones: Universidad La Salle