ArticlePloS one2019
ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Evolving strategies for virus discovery.Microbial genomics · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- The Human Virome in Infectious Diseases: Insights from Chronic and Acute Infections Across Body Sites-A Narrative Review.Microorganisms · 2026Review
- Ensemble Deep Learning Models on Raw DNA Sequences for Viral Genome Identification in Human Samples.Sensors (Basel, Switzerland) · 2026Article
- ViTrace detects viral signatures in tumor transcriptomes using a hybrid language model.Communications biology · 2025Article
- VITALdb: to select the best viroinformatics tools for a desired virus or application.Briefings in bioinformatics · 2025Article
- HPV-KITE: sequence analysis software for rapid HPV genotype detection.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 2025Article
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 2025Review
- VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.Briefings in bioinformatics · 2024Article
- DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification.BMC bioinformatics · 2024Article
- Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family.BMC bioinformatics · 2024Article
- Optimized model architectures for deep learning on genomic data.Communications biology · 2024Article
- VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.Bioinformatics (Oxford, England) · 2024Article
- Benchmarking informatics approaches for virus discovery: caution is needed when combiningmSystems · 2024Article
- Hecatomb: an integrated software platform for viral metagenomics.GigaScience · 2024Article
- HostNet: improved sequence representation in deep neural networks for virus-host prediction.BMC bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as "unknown" by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.