In this section
@article{Koch:2026:10.1002/smtd.202502335,
author = {Koch, C and Sakthimani, S and Noakes, VM and Cretu, M and Newman, D and Gutierrez, R and Bruce, M and Gorelik, J and Guerra, N and Edel, JB and Ivanov, AP},
doi = {10.1002/smtd.202502335},
journal = {Small Methods},
title = {Machine learning-driven nanopore sensing for quantitative, label-free miRNA detection},
url = {http://dx.doi.org/10.1002/smtd.202502335},
volume = {10},
year = {2026}
}
TY - JOUR
AB - 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.
AU - Koch,C
AU - Sakthimani,S
AU - Noakes,VM
AU - Cretu,M
AU - Newman,D
AU - Gutierrez,R
AU - Bruce,M
AU - Gorelik,J
AU - Guerra,N
AU - Edel,JB
AU - Ivanov,AP
DO - 10.1002/smtd.202502335
PY - 2026///
SN - 2366-9608
TI - Machine learning-driven nanopore sensing for quantitative, label-free miRNA detection
T2 - Small Methods
UR - http://dx.doi.org/10.1002/smtd.202502335
VL - 10
ER -