Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning.
Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning.
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DOI：
10.3233/shti220550
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发表时间：
2022-05-25
影响因子：
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通讯作者：
Bellazzi, Riccardo
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文献类型：
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作者：
Nicora, Giovanna;Marini, Simone;Bellazzi, Riccardo
在这项工作中，我们表明，增量机器学习可用于预测新出现的SARS-CoV-2谱系的分类，动态区分中性变体和非中性变体，即感兴趣的变体或关注的变体。从GISAID数据库中收集的刺突蛋白一级序列开始，我们已经推导出一组k-mer特征，即，具有固定长度k的氨基酸序列。然后，我们实施了逻辑回归增量学习器，每月对自2020年2月至2021年10月收集的变量进行测试。分类器的平衡准确率平均值为0.72 ± 0.2，最近12个月提高到0.78 ± 0.16。α、β、γ、η、κ和δ变体被识别为非中性变体，平均召回率为90%。总之，增量学习被证明是流行病监测的有用工具，因为它能够随着时间的推移根据新数据更新模型。
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.