Spike2Vec：一种有效的可扩展的COVID-19棘波序列嵌入方法
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences
摘要：
随着COVID-19在全球的快速传播，越来越多与该病毒相关的数据变得可用，包括基因组序列数据。目前，在GISAID等平台上公开的基因组序列总数为数百万，并且每天都在增加。这种\emph{Big Data}的可用性为研究人员详细研究这种病毒创造了新的机会。这对于出现和传播的COVID-19变体的所有动态尤为重要。这一丰富的数据源将使我们了解针对这一流行病和未来流行病威胁进行基因组监测的最佳方法，最终目标是减轻或消除此类威胁。分析和处理数百万个基因组序列是一项具有挑战性的任务。尽管传统的序列分类方法被证明是有效的，但它们并不是被设计来处理这些特定类型的基因组序列。此外，大多数现有方法还面临可扩展性问题。针对冠状病毒基因组数据定制的先前研究提出使用尖峰序列（对应于基因组的子序列）而不是使用完整的基因组序列来执行不同的机器学习（ML）任务，例如分类和聚类。然而，这些方法存在可扩展性问题。在本文中，我们提出了一种称为Spike 2 Vec的方法，这是一种有效且可扩展的特征向量表示，可用于下游ML任务。通过实验，我们证明了Spike 2 Vec不仅在几百万个尖峰序列上具有可扩展性，而且在预测精度、F1得分等方面都优于基线模型。
英文摘要：
With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such \emph{Big Data} creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues. In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc.
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