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 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.