Different data analysis models for detecting miRNAs using nanopores

Abstract

Nanopore sensors offer exceptional sensitivity for detecting single molecules, making them ideal for early disease diagnostics. In this study, we present a multiplexed nanopore-based assay that combines DNA-barcoded probes with advanced computational analysis to detect microRNAs (miRNAs) with high specificity and quantitative accuracy. Each probe binds selectively to its target biomarker and generates a characteristic delay in the ionic current signal upon translocation through the nanopore, enabling label-free detection. We evaluated three analytical strategies for classifying delayed versus non-delayed events: (1) moving standard deviation (MSD), (2) spectral entropy (SE), and (3) a convolutional neural network (CNN). While MSD and SE rely on manually defined thresholds and exhibit limited sensitivity, the CNN model, trained on image representations of raw current traces, achieved near-perfect classification performance across all metrics. Grad-CAM visualisation confirmed that the CNN focused on biophysically relevant signal regions, enhancing interpretability and generalisability. All methods produced sigmoidal concentration-response curves consistent with expected binding kinetics, and nanopore-derived delay metrics closely matched RT-qPCR validation data. All three methods were capable of distinguishing between signal classes; however, the CNN model demonstrated superior sensitivity and robustness. This work highlights the importance of data interpretation in nanopore sensing and presents a comparative framework for binary event classification. The findings pave the way for the development of machine learning-driven nanopore diagnostics capable of detecting diverse biomarker types at the single-molecule level.