Machine learning-driven nanopore sensing for quantitative, label-free miRNA detection
Author(s)
Type
Journal Article
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 selectively binds its target biomarker and induces 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 (accuracy = 0.99, precision = 0.99, recall = 0.99). 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.
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 (accuracy = 0.99, precision = 0.99, recall = 0.99). 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.
Date Issued
2026-02-23
Date Acceptance
2026-01-11
Citation
Small Methods, 2026, 10 (4)
ISSN
2366-9608
Publisher
Wiley
Journal / Book Title
Small Methods
Volume
10
Issue
4
Copyright Statement
© 2026 The Author(s). Small Methods published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1002/smtd.202502335
Subjects
biomarker
data-analysis
machine-learning
miRNAs
nanopores
Publication Status
Published
Article Number
e02335
Date Publish Online
2026-01-19