ArticleBMJ health & care informatics2022
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.
Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From sequences to strategies: Early detection of new SARS-CoV-2 variants via genetic distance to reduce hospitalizations.PLoS computational biology · 2026Article
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.Briefings in bioinformatics · 2025Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 2025Article
- Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.bioRxiv : the preprint server for biology · 2024Article
- Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.Briefings in bioinformatics · 2024Article
- A GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic.Journal of imaging · 2024Article
- Anomaly Detection Models for SARS-CoV-2 Surveillance Based on GenomeMicroorganisms · 2023Article
- Novel Entropy-Based Phylogenetic Algorithm: A New Approach for Classifying SARS-CoV-2 Variants.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
objectivesThe objective of this study is the implementation of an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)).
methodsWe downloaded spike protein primary sequences from the public resource GISAID and we represented each sequence as k-mer counts. For each week since 1 July 2020, we evaluate if each sequence represents an anomaly based on a One Class support vector machine (SVM) classification algorithm trained on neutral protein sequences collected from February to June 2020.
resultsWe assess the ability of the One Class classifier to detect known VOC and VOI, such as Alpha, Delta or Omicron, ahead of their official classification by health authorities. In median, the classifier predicts a non-neutral variant as outlier 10 weeks before the official date of designation as VOC/VOI. DISCUSSION: The identification of non-neutral variants during a pandemic usually relies on indicators available during time, such as changing population size of a variant. Automatic variant surveillance systems based on protein sequences can enhance the fast identification of variants of potential concern.
conclusionMachine learning, and in particular One Class SVM classification, can support the detection of potentially VOC/VOI variants during an evolving pandemics.
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