ArticleTransboundary and emerging diseases2019
Rapid identification of human-infecting viruses.
Article in Transboundary and emerging diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 46 citations in OpenAlex.
- An improved dataset for predicting mammal infecting viruses from genetic sequence information.PLoS computational biology · 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
- Hidden challenges in evaluating spillover risk of zoonotic viruses using machine learning models.Communications medicine · 2025Article
- HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences.GigaScience · 2025Article
- RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes.GigaScience · 2024Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Characterisation of putative novel tick viruses and zoonotic risk prediction.Ecology and evolution · 2024Article
- PacBio sequencing of human fecal samples uncovers the DNA methylation landscape of 22 673 gut phages.Nucleic acids research · 2023Article
- Prototype Pathogens for Vaccine and Monoclonal Antibody Countermeasure Development: NIAID Workshop Process and Outcomes for Viral Families of Pandemic Potential.The Journal of infectious diseases · 2023Article
- A deep learning approach reveals unexplored landscape of viral expression in cancer.Nature communications · 2023Article
- (Bioinformatics advances · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Advancing development of medical countermeasures: Incorporating COVID-19 lessons learned into future pandemic preparedness planning.Human vaccines & immunotherapeutics · 2022Article
- The science of the host-virus network.Nature microbiology · 2021Review
- Article
- Computational Viromics: Applications of the Computational Biology in Viromics Studies.Virologica Sinica · 2021Article
- Identifying and prioritizing potential human-infecting viruses from their genome sequences.PLoS biology · 2021Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics. However, there is still a lack of methodology for rapidly identifying novel viruses with the potential of human infection. This study built several machine learning models to discriminate human-infecting viruses from other viruses based on the frequency of k-mers in the viral genomic sequences. The k-nearest neighbor (KNN) model can predict the human-infecting viruses with an accuracy of over 90%. The performance of this KNN model built on the short contigs (≥1 kb) is comparable to those built on the viral genomes. We used a reported human blood virome to further validate this KNN model with an accuracy of over 80% based on very short raw reads (150 bp). Our work demonstrates a conceptual and generic protocol for the discovery of novel human-infecting viruses in viral metagenomics studies.
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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.