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

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.