标题： 基于神经形态脉冲神经网络的冠状病毒刺突序列分类 显示英文标题

标题： Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences

摘要： SARS-CoV-2（严重急性呼吸综合征冠状病毒2）在新冠疫情后病毒数据的可用性呈指数级增长，达到了巨大的规模，为研究其行为打开了新的研究大门。研究人员进行了各种研究，以更深入地了解该病毒，如基因组监测等，以便开发有效的预防机制。然而，该病毒的不稳定性（快速突变、多种宿主等）在为其设计分析系统时带来了挑战。因此，我们提出了一种基于神经网络（NN）的机制，以对SARS-CoV-2数据进行高效的分析，因为NN在训练后能表现出通用的行为。此外，我们不是使用病毒的全长基因组，而是将我们的方法应用于其刺突区域，因为该区域已知具有主要的突变，并用于附着到宿主细胞膜上。在本文中，我们介绍了一个流程，首先将刺突蛋白序列转换为固定长度的数值表示，然后使用类脑脉冲神经网络对这些序列进行分类。我们使用真实世界的SARS-CoV-2刺突序列数据将我们方法的性能与各种基线方法进行了比较，并表明我们的方法相比最近的基线方法能够实现更高的预测准确性。 显示英文摘要

摘要： The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, etc, so that efficient prevention mechanisms can be developed. However, the unstable nature of the virus (rapid mutations, multiple hosts, etc) creates challenges in designing analytical systems for it. Therefore, we propose a neural network-based (NN) mechanism to perform an efficient analysis of the SARS-CoV-2 data, as NN portrays generalized behavior upon training. Moreover, rather than using the full-length genome of the virus, we apply our method to its spike region, as this region is known to have predominant mutations and is used to attach to the host cell membrane. In this paper, we introduce a pipeline that first converts the spike protein sequences into a fixed-length numerical representation and then uses Neuromorphic Spiking Neural Network to classify those sequences. We compare the performance of our method with various baselines using real-world SARS-CoV-2 spike sequence data and show that our method is able to achieve higher predictive accuracy compared to the recent baselines.