ArticleGigaScience2024
RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes.
Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage-host prediction.Briefings in bioinformatics · 2025Pooled it
- Diversity of Antarctic sea ice and under-ice seawater RNA viruses.Applied and environmental microbiology · 2026Article
- GiantHost: a domain-adaptive and uncertainty-aware framework for giant virus host prediction.Bioinformatics (Oxford, England) · 2026Article
- Preparation method shapes the recovery and ecological interpretation of DNA and RNA soil viral communities.Nature communications · 2026Article
- Salinity-driven niche partitioning of aquatic viruses in one of Europe's largest estuaries.Applied and environmental microbiology · 2026Article
- PhaBOX2: an enhanced web server for discovering and analyzing viral contigs in metagenomic data.Nucleic acids research · 2026Article
- A comprehensive RNA virome from molluscan transcriptomes reveals extensive diversity and modular genome evolution.Virologica Sinica · 2026Article
- Metatranscriptomic discovery of a novel viral class from the Yangshan deep-water port virosphere.Archives of virology · 2026Article
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- Virome diversity, evolution, transmission networks, and zoonotic potential of wildlife on the Qinghai-Tibet plateau.NPJ biofilms and microbiomes · 2026Article
- Experimental insights in taxon-specific functional responses to droughts in glacier-fed stream biofilms.Microbiome · 2026Article
- SARS-CoV-2 wastewater genomic surveillance: approaches, challenges, and opportunities.Genome biology · 2026Review
- Comparative virome analysis of lake and domestic wastewater revealed the unexpected presence of swine acute diarrhea syndrome coronavirus andFrontiers in microbiology · 2026Article
- Viral Sentry AI-Automated zoonotic surveillance and drug repurposing agent.Biology methods & protocols · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- The effect of taxonomic, host-dependent features and sample bias on virus host prediction using machine learning and short sequence k-mers.Scientific reports · 2025Article
- Review
- RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes.GigaScience · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity.
resultsIn this study, we introduce RNAVirHost, a machine learning-based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%.
conclusionsThe application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.
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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.