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Lightweight Murmur2Vec embeddings match heavy PLMs in biological classification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Murmur2Vec, a lightweight and efficient embedding method for biological sequence classification that rivals the performance of larger, computationally intensive protein language models (PLMs) like ESM-2. This new approach uses a hashing sketch to aggregate k-mer counts, offering a theoretically grounded alternative that is practical for large-scale genomic surveillance on commodity hardware. In comparative tests across four classification tasks, including SARS-CoV-2 spike lineage and HIV-1 subtype, Murmur2Vec matched or outperformed fine-tuned ESM-2 models, demonstrating its effectiveness and efficiency. AI

IMPACT Offers a more efficient and accessible alternative for biological sequence classification, potentially accelerating large-scale genomic surveillance.

RANK_REASON The cluster contains an academic paper detailing a new method and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lightweight Murmur2Vec embeddings match heavy PLMs in biological classification

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The cluster contains an academic paper detailing a new method and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sarwan Ali, Taslim Murad, Imdadullah Khan, Safi Faizullah ·

    When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification

    arXiv:2512.10147v2 Announce Type: replace Abstract: \textbf{Motivation:} Pre-trained protein language models (PLMs) such as ESM-2 have become the default representation for biological sequence tasks, but they are computationally heavy and require GPUs both for embedding and for f…

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