Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.
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DOI：
10.1136/bmjhci-2022-100643
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发表时间：
2022-12
影响因子：
4.1
通讯作者：
Bellazzi, Riccardo
中科院分区：
文献类型：
--
作者：
Nicora, Giovanna;Salemi, Marco;Marini, Simone;Bellazzi, Riccardo
本研究的目的是实施每周检测新的SARS-CoV-2变体和非中性变体（关注变体（VOC）和感兴趣变体（VOI））的自动程序。我们从公共资源GISAID下载刺突蛋白一级序列，并将每个序列表示为k-mer计数。自2020年7月1日起的每周，我们根据一类支持向量机（SVM）分类算法评估每个序列是否代表异常，该算法对2020年2月至6月收集的中性蛋白质序列进行了训练。我们评估了One Class分类器检测已知VOC和VOI（如Alpha、Delta或Omicron）的能力，然后再由卫生当局对其进行正式分类。在中位数中，分类器在指定为VOC/VOI的正式日期前10周将非中性变量预测为离群值。在大流行期间识别非中性变体通常依赖于在一段时间内可用的指标，例如变体的变化群体规模。基于蛋白质序列的自动变异监测系统可以提高潜在问题变异的快速识别。机器学习，特别是一类SVM分类，可以支持在不断发展的流行病期间检测潜在的VOC/VOI变体。
The 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)). We 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. We 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. 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. Machine 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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