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

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...

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Detalles Bibliográficos
Autor principal:	Machaca Arceda, Vicente Enrique
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2020
Materias:	Ciencias Informáticas CNN Genome Viral subtyping k-mer Kameris Castor ML-DSP Genoma Subtipos de virus
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/108009
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-108009
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas CNN Genome Viral subtyping k-mer Kameris Castor ML-DSP Genoma Subtipos de virus
spellingShingle	Ciencias Informáticas CNN Genome Viral subtyping k-mer Kameris Castor ML-DSP Genoma Subtipos de virus Machaca Arceda, Vicente Enrique An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
topic_facet	Ciencias Informáticas CNN Genome Viral subtyping k-mer Kameris Castor ML-DSP Genoma Subtipos de virus
description	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.
format	Articulo Articulo
author	Machaca Arceda, Vicente Enrique
author_facet	Machaca Arceda, Vicente Enrique
author_sort	Machaca Arceda, Vicente Enrique
title	An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
title_short	An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
title_full	An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
title_fullStr	An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
title_full_unstemmed	An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification
title_sort	analysis of k-mer frequency features with svm and cnn for viral subtyping classification
publishDate	2020
url	http://sedici.unlp.edu.ar/handle/10915/108009
work_keys_str_mv	AT machacaarcedavicenteenrique ananalysisofkmerfrequencyfeatureswithsvmandcnnforviralsubtypingclassification AT machacaarcedavicenteenrique unanalisisdeatributosdefrecuenciadekmerconsvmycnnparalaclasificaciondesubtiposdevirus AT machacaarcedavicenteenrique analysisofkmerfrequencyfeatureswithsvmandcnnforviralsubtypingclassification
bdutipo_str	Repositorios
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An analysis of k-mer frequency features with SVM and CNN for viral subtyping classification

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