Multi-modal transformer for signal classification in nanopore blockade experiments

Abstract:Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ionic current as analytes enter nanometer-scale pores, providing a means of identifying diverse biomarkers from their characteristic signal patterns. However, these signals are highly complex, and reliably assigning them to specific molecules remains a major challenge. Here, we address this by introducing a multi-modal deep learning architecture that jointly processes multiple signal representations, including raw time-series data, wavelet-based images, and static feature vectors. Our approach surpasses existing methods by more than 10 percentage points on a 42-peptide benchmark and transfers to a 20-amino-acid dataset with near-perfect accuracy. The model integrates complementary information from these representations, with attention analysis showing that the time-series and wavelet-image inputs emphasize different features of the same event. Together, these results demonstrate the potential of machine learning to enable robust, high-accuracy molecular identification with nanopore sensors.

22 pages (incl. references), 8 figures

Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Biomolecules (q-bio.BM)

arXiv:2607.20323 [cs.LG]

(or arXiv:2607.20323v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20323 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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