Multi-modal transformer for signal classification in nanopore blockade experiments
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 percent...
| Authors | Sandro Kuppel, Julian Hoßbach, Samuel Tovey et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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