ArticleDatabase : the journal of biological databases and curation2021
COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.
Article in Database : the journal of biological databases and curation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Combinatorial analysis of clinical and genomic data used to assess the association between SARS-CoV-2 mutations and disease severity.PNAS nexus · 2026Article
- Prediction models for COVID-19 disease outcomes.Emerging microbes & infections · 2024Article
- Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.Scientific reports · 2024Article
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- Applying the digital data and the bioinformatics tools in SARS-CoV-2 research.Computational and structural biotechnology journal · 2023Review
- Article
- Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.International journal of molecular sciences · 2022Article
- Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?Journal of molecular biology · 2022Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning.Journal of clinical medicine · 2022Article
- SARS-CoV-2 Variants and Clinical Outcomes: A Systematic Review.Life (Basel, Switzerland) · 2022Review
- SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.Global health, epidemiology and genomics · 2022Article
- Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.
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Registered trials
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