Literature DB >> 24372032

Comparing virus classification using genomic materials according to different taxonomic levels.

Jing-Doo Wang1.   

Abstract

In this paper, three genomic materials--DNA sequences, protein sequences, and regions (domains) are used to compare methods of virus classification. Virus classes (categories) are divided by various taxonomic level of virus into three datasets for 6 order, 42 family, and 33 genera. To increase the robustness and comparability of experimental results of virus classification, the classes are selected that contain at least 10 instances, and meanwhile each instance contains at least one region name. Experimental results show that the approach using region names achieved the best accuracies--reaching 99.9%, 97.3%, and 99.0% for 6 orders, 42 families, and 33 genera, respectively. This paper not only involves exhaustive experiments that compare virus classifications using different genomic materials, but also proposes a novel approach to biological classification based on molecular biology instead of traditional morphology.

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Year:  2013        PMID: 24372032     DOI: 10.1142/S0219720013430038

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  2 in total

1.  An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.

Authors:  Stephen Solis-Reyes; Mariano Avino; Art Poon; Lila Kari
Journal:  PLoS One       Date:  2018-11-14       Impact factor: 3.240

2.  On the Verge of Life: Distribution of Nucleotide Sequences in Viral RNAs.

Authors:  Mykola Husev; Andrij Rovenchak
Journal:  Biosemiotics       Date:  2021-02-17       Impact factor: 0.711

  2 in total

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