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

2025-12-10

AI Summary

To address the computational inefficiency, poor scalability, and reliance on multiple sequence alignment or deep learning models in large-scale SARS-CoV-2 spike protein sequence analysis, this paper proposes an alignment-free, low-overhead hashing-based embedding method. Specifically, it introduces the first application of MurmurHash3 for direct k-mer spectrum hashing, followed by PCA dimensionality reduction and lightweight classification (XGBoost, SVM, or MLP). The method achieves O(n) linear time complexity, with per-sequence embedding completed in milliseconds—99.81% faster than state-of-the-art approaches. On a million-sequence benchmark, it attains 86.4% lineage classification accuracy, demonstrating exceptional trade-offs among computational efficiency, scalability, and discriminative power.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Data Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Data transparency and provenance

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.

Problem

• Develops scalable embedding method for COVID-19 spike sequences
• Addresses computational inefficiency in existing viral sequence analysis
• Enables fast, accurate lineage classification using machine learning

Innovation

• Uses hashing to generate compact spike sequence embeddings
• Trains machine learning models for lineage classification
• Achieves high accuracy with drastically reduced generation time