ArticleBMC bioinformatics2018
Machine Learning for detection of viral sequences in human metagenomic datasets.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bioinformatics Tools and Approaches for Virus Discovery in Genomic Data: A Systematic Review.Viruses · 2025Pooled it
- Ensemble Deep Learning Models on Raw DNA Sequences for Viral Genome Identification in Human Samples.Sensors (Basel, Switzerland) · 2026Article
- Mapping evolutionary paradigm of bovine viral diarrhea virusVirulence · 2025Article
- VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.Briefings in bioinformatics · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2.BMC bioinformatics · 2024Article
- Agnostic Sequencing for Detection of Viral Pathogens.Clinical microbiology reviews · 2023Review
- Article
- SARS-CoV-2 virus classification based on stacked sparse autoencoder.Computational and structural biotechnology journal · 2023Article
- Defining Novel DNA Virus-Tumor Associations and Genomic Correlates Using Prospective Clinical Tumor/Normal Matched Sequencing Data.The Journal of molecular diagnostics : JMD · 2022Article
- Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences.mSystems · 2022Article
- Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.Contrast media & molecular imaging · 2022Review
- Explainable deep neural networks for novel viral genome prediction.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses.Scientific reports · 2021Article
- Taxonomic, functional and expression analysis of viral communities associated with marine sponges.PeerJ · 2021Article
- A novel data augmentation approach for mask detection using deep transfer learning.Intelligence-based medicine · 2021Article
- Non-adaptive Evolution of Trimeric Autotransporters inFrontiers in microbiology · 2020Article
- Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses.PloS one · 2020Article
- Evaluation of Sequencing Library Preparation Protocols for Viral Metagenomic Analysis from Pristine Aquifer Groundwaters.Viruses · 2019Article
- ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.PloS one · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundDetection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data.
resultsWe trained Random Forest and Artificial Neural Network using metagenomic sequences taxonomically classified into virus and non-virus classes. The algorithms achieved accuracies well beyond chance level, with area under ROC curve 0.79. Two codons (TCG and CGC) were found to have a particularly strong discriminative capacity.
conclusionRSCU-based machine learning techniques applied to metagenomic sequencing data can help identify a large number of putative viral sequences and provide an addition to conventional methods for taxonomic classification.
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