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

轻量级 Murmur2Vec 嵌入在生物分类中可媲美大型 PLM

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 Murmur2Vec，一种用于生物序列分类的轻量级高效嵌入方法，其性能可与 ESM-2 等大型、计算密集型蛋白质语言模型 (PLM) 相媲美。这种新方法使用哈希草图来聚合 k-mer 计数，提供了一种理论上可靠的替代方案，适用于在普通硬件上进行大规模基因组监测。在包括 SARS-CoV-2 刺突蛋白谱系和 HIV-1 亚型在内的四项分类任务的比较测试中，Murmur2Vec 的表现与微调后的 ESM-2 模型相当或更优，证明了其有效性和效率。 AI

影响 为生物序列分类提供了更高效、更易于访问的替代方案，有望加速大规模基因组监测。

排序理由 该集群包含一篇详细介绍新方法和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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轻量级 Murmur2Vec 嵌入在生物分类中可媲美大型 PLM

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该集群包含一篇详细介绍新方法和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    廉价嵌入何时能胜过蛋白质语言模型？一种基于理论的生物序列分类哈希草图

    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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