Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences
Journal:
arXiv
Published Date:
Dec 19, 2024
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.
