Murmur2Vec: A Hashing Based Solution For Embedding Generation Of COVID-19 Spike Sequences

• URL: http://arxiv.org/abs/2512.10147v1
• Date: Wed, 10 Dec 2025 23:03:10 GMT
• Title: Murmur2Vec: A Hashing Based Solution For Embedding Generation Of COVID-19 Spike Sequences
• Authors: Sarwan Ali, Taslim Murad,
• Abstract summary: Early detection and characterization of coronavirus disease (COVID-19), caused by SARS-CoV-2, remain critical for effective clinical response and public-health planning.<n>Existing approaches face notable limitations. Phylogenetic tree-based methods are computationally intensive and do not scale efficiently to today's multi-million-sequence datasets.<n>In this study, we focus on the most prevalent SARS-CoV-2 lineages associated with the spike protein region and introduce a scalable embedding method that leverages hashing to generate compact, low-dimensional representations of spike sequences.
• Score: 4.970277730082774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early detection and characterization of coronavirus disease (COVID-19), caused by SARS-CoV-2, remain critical for effective clinical response and public-health planning. The global availability of large-scale viral sequence data presents significant opportunities for computational analysis; however, existing approaches face notable limitations. Phylogenetic tree-based methods are computationally intensive and do not scale efficiently to today's multi-million-sequence datasets. Similarly, current embedding-based techniques often rely on aligned sequences or exhibit suboptimal predictive performance and high runtime costs, creating barriers to practical large-scale analysis. In this study, we focus on the most prevalent SARS-CoV-2 lineages associated with the spike protein region and introduce a scalable embedding method that leverages hashing to generate compact, low-dimensional representations of spike sequences. These embeddings are subsequently used to train a variety of machine learning models for supervised lineage classification. We conduct an extensive evaluation comparing our approach with multiple baseline and state-of-the-art biological sequence embedding methods across diverse metrics. Our results demonstrate that the proposed embeddings offer substantial improvements in efficiency, achieving up to 86.4\% classification accuracy while reducing embedding generation time by as much as 99.81\%. This highlights the method's potential as a fast, effective, and scalable solution for large-scale viral sequence analysis.

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