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

TL;DR

This paper introduces a neuromorphic spiking neural network approach to classify SARS-CoV-2 spike sequences, achieving higher accuracy than existing methods, aiding in understanding virus mutations.

Contribution

It presents a novel pipeline converting spike sequences into fixed-length representations and applying spiking neural networks for classification, improving accuracy over baselines.

Findings

Higher predictive accuracy than recent baselines

Effective classification of SARS-CoV-2 spike sequences

Potential for improved viral mutation analysis

Abstract

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.