Machine Learning-Driven Nanopore Sensing for Quantitative, Label-Free miRNA Detection.

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 accuracy. Each probe selectively binds its target biomarker and induces a characteristic delay in the ionic current signal upon translocation through the nanopore. 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 r

SUBMITTER: Koch C 

PROVIDER: S-EPMC12929938 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature