ArticlePloS one2013
Real time classification of viruses in 12 dimensions.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- CAKR: commutative algebra k-mer representations for genomics.Nature communications · 2026Article
- Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Comparative study of encoded and alignment-based methods for virus taxonomy classification.Scientific reports · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time.Genes · 2022Article
- Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.Neural computing & applications · 2022Article
- The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.Entropy (Basel, Switzerland) · 2021Article
- A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes.Computational and structural biotechnology journal · 2021Article
- Geometric construction of viral genome space and its applications.Computational and structural biotechnology journal · 2021Article
- Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks.Nature biotechnology · 2019Article
- SWSPM: A Novel Alignment-Free DNA Comparison Method Based on Signal Processing Approaches.Evolutionary bioinformatics online · 2019Article
- A Novel Approach to Clustering Genome Sequences Using Inter-nucleotide Covariance.Frontiers in genetics · 2019Article
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- Integrated human-virus metabolic stoichiometric modelling predicts host-based antiviral targets against Chikungunya, Dengue and Zika viruses.Journal of the Royal Society, Interface · 2018Article
- An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.PloS one · 2018Article
- Establishing the phylogeny of Prochlorococcus with a new alignment-free method.Ecology and evolution · 2017Article
- A machine learning approach for viral genome classification.BMC bioinformatics · 2017Article
- Virus Database and Online Inquiry System Based on Natural Vectors.Evolutionary bioinformatics online · 2017Article
- Ebolavirus classification based on natural vectors.DNA and cell biology · 2015Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing label rates in GenBank are 12.1% for family label and 30.0% for genus label. Using the proposed Natural Vector representation, all 2,044 single-segment referenced viral genomes in GenBank can be embedded in [Formula: see text]. Unlike other approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family, subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides a visualization of the distribution of viruses in [Formula: see text]. Unlike the commonly used tree visualization methods which suffer from uniqueness and existence problems, our representation always exists and is unique. This approach is successfully used to predict and correct viral classification information, as well as to identify viral origins; e.g. a recent public health threat, the West Nile virus, is closer to the Japanese encephalitis antigenic complex based on our visualization. Based on cross-validation results, the accuracy rates of our predictions are as high as 98.2% for Baltimore class labels, 96.6% for family labels, 99.7% for subfamily labels and 97.2% for genus labels.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.