ArticleScientific reports2018
Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influenza virus genotype to phenotype predictions through machine learning: a systematic review.Emerging microbes & infections · 2021Pooled it
- Alignment-Free Genome Geometry and Mosaic Barcodes Characterize Modular Diversity in the Cacao Swollen Shoot Virus Complex.Viruses · 2026Article
- Deciphering the Code of Viral-Host Adaptation Through Maximum-Entropy Nucleotide Bias Models.Molecular biology and evolution · 2025Article
- General Intelligence Framework to Predict Virus Adaptation Based on a Genome Language Model.Research (Washington, D.C.) · 2025Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Using machine learning to detect coronaviruses potentially infectious to humans.Scientific reports · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.PLoS pathogens · 2021Article
- Interpretable detection of novel human viruses from genome sequencing data.NAR genomics and bioinformatics · 2021Article
- Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.Journal of medical systems · 2020Review
- Predicting host taxonomic information from viral genomes: A comparison of feature representations.PLoS computational biology · 2020Article
- Rapid identification of human-infecting viruses.Transboundary and emerging diseases · 2019Article
- Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus.Scientific reports · 2019Article
- Reads Binning Improves Alignment-Free Metagenome Comparison.Frontiers in genetics · 2019Article
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2 authors.
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Abstract
Predicting the hosts of newly discovered viruses is important for pandemic surveillance of infectious diseases. We investigated the use of alignment-based and alignment-free methods and support vector machine using mononucleotide frequency and dinucleotide bias to predict the hosts of viruses, and applied these approaches to three datasets: rabies virus, coronavirus, and influenza A virus. For coronavirus, we used the spike gene sequences, while for rabies and influenza A viruses, we used the more conserved nucleoprotein gene sequences. We compared the three methods under different scenarios and showed that their performances are highly correlated with the variability of sequences and sample size. For conserved genes like the nucleoprotein gene, longer k-mers than mono- and dinucleotides are needed to better distinguish the sequences. We also showed that both alignment-based and alignment-free methods can accurately predict the hosts of viruses. When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses.
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