ArticlePloS one2020
Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 145 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
145 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.Journal of medical Internet research · 2020Pooled it
- Genome-wide pervasiveness and localized variation of [Formula: see text]-mer-based genomic signatures in eukaryotes.Scientific reports · 2026Article
- Deciphering 6-mer Spectra Distribution Rules in Coronavirus Genomes: Application to Comparative Genomic Analysis.International journal of molecular sciences · 2026Article
- Ensemble Deep Learning Models on Raw DNA Sequences for Viral Genome Identification in Human Samples.Sensors (Basel, Switzerland) · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Life at the extremes: maximally divergent microbes with similar genomic signatures linked to extreme environments.NAR genomics and bioinformatics · 2025Article
- Article
- Rapid, On-Site SARS-CoV‑2 Variant Detection and Differentiation Using GLAD-Pristine Silver Nanorod Arrays and Machine Learning-Enhanced SERS.ACS omega · 2025Article
- Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.Scientific reports · 2025Article
- 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
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family.BMC bioinformatics · 2024Article
- Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.BMC bioinformatics · 2024Article
- PC-mer: An Ultra-fast memory-efficient tool for metagenomics profiling and classification.PloS one · 2024Article
- Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.PloS one · 2024Article
- SpecGMM: Integrating Spectral analysis and Gaussian Mixture Models for taxonomic classification and identification of discriminative DNA regions.Bioinformatics advances · 2024Article
- Polarization- and Chaos-Game-Based Fingerprinting of Molecular Targets of Listeria Monocytogenes Vaccine and Fully Virulent Strains.Current issues in molecular biology · 2023Article
- Environment and taxonomy shape the genomic signature of prokaryotic extremophiles.Scientific reports · 2023Article
- Engineering Materials and Devices for the Prevention, Diagnosis, and Treatment of COVID-19 and Infectious Diseases.Nanomaterials (Basel, Switzerland) · 2023Review
85 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman's rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.
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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.