ArticleStudies in health technology and informatics2022
Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning.
Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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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.
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Who cites it
1 citing paper in PubMed.
- Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.BMJ health & care informatics · 2022Article
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4 authors.
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Abstract
In this work we show that Incremental Machine Learning can be used to predict the classification of emerging SARS-CoV-2 lineages, dynamically distinguishing between neutral variants and non-neutral ones, i.e. variants of interest or variants of concerns. Starting from the Spike protein primary sequences collected in the GISAID db, we have derived a set of k-mers features, i.e., aminoacid subsequences with fixed length k. We have then implemented a Logistic Regression Incremental Learner that was monthly tested on the variants collected since February 2020 until October 2021. The average value of balanced accuracy of the classifier is 0.72 ± 0.2, which increased to 0.78 ± 0.16 in the last 12 months. The alpha, beta, gamma, eta, kappa and delta variants were recognized as non-neutral variants with mean recall ∼90%. In summary, incremental learning proved to be a useful instrument for pandemic surveillance, given its capability to update the model on new data over time.
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