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<p>Nanopore sensing holds transformative potential for revolutionizing protein and glycan sequencing. However, translating this potential into practical, high-fidelity identification is severely bottlenecked by the challenge of processing massive amounts of highly similar nanopore ionic-current data, spurring an urgent need for robust, AI-driven solutions. Prevailing deep learning methods suffer from two limitations: they often fail to capture the fine-grained temporal dynamics essential for distinguishing structurally similar analytes, and their generic training strategies inadequately extract weak discriminative features, thus limiting classification precision. Here, we present SEDA-Former (Signal Enhancement and Dynamic Attention Transformer), a deep temporal learning framework designed for high-resolution nanopore single-molecule identification. SEDA-Former incorporates a multi-window sliding standard-deviation method for feature enhancement, a multi-channel temporal convolutional network to mine weak features in temporal dynamics, and a progressive adaptive attention training strategy that dynamically reweights sample losses based on learning difficulty. Across a diverse set of challenging benchmark datasets, including nanopore signals of 15 glycosides, 24 ginsenosides, 8 DNA molecules, and 17 cholic acid conjugates, spanning varying levels of signal complexity, SEDA-Former consistently achieves substantially higher classification accuracy than state-of-the-art methods and demonstrates robust cross-dataset transferability. SEDA-Former provides a versatile and scalable solution to facilitate single-molecule identification in nanopore sensing.</p>

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A universal deep learning framework for empowering nanopore identification by reinforcing temporal signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

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  • Ming Li,
  • Minmin Li,
  • Yuchen Cao,
  • Jing Wang,
  • Hanwen Ning,
  • Guangyan Qing

摘要

Nanopore sensing holds transformative potential for revolutionizing protein and glycan sequencing. However, translating this potential into practical, high-fidelity identification is severely bottlenecked by the challenge of processing massive amounts of highly similar nanopore ionic-current data, spurring an urgent need for robust, AI-driven solutions. Prevailing deep learning methods suffer from two limitations: they often fail to capture the fine-grained temporal dynamics essential for distinguishing structurally similar analytes, and their generic training strategies inadequately extract weak discriminative features, thus limiting classification precision. Here, we present SEDA-Former (Signal Enhancement and Dynamic Attention Transformer), a deep temporal learning framework designed for high-resolution nanopore single-molecule identification. SEDA-Former incorporates a multi-window sliding standard-deviation method for feature enhancement, a multi-channel temporal convolutional network to mine weak features in temporal dynamics, and a progressive adaptive attention training strategy that dynamically reweights sample losses based on learning difficulty. Across a diverse set of challenging benchmark datasets, including nanopore signals of 15 glycosides, 24 ginsenosides, 8 DNA molecules, and 17 cholic acid conjugates, spanning varying levels of signal complexity, SEDA-Former consistently achieves substantially higher classification accuracy than state-of-the-art methods and demonstrates robust cross-dataset transferability. SEDA-Former provides a versatile and scalable solution to facilitate single-molecule identification in nanopore sensing.