Brownian motion data augmentation: a method to push neural network performance on nanopore sensors.
Journal:
Bioinformatics (Oxford, England)
Published Date:
May 29, 2025
Abstract
MOTIVATION: Nanopores are highly sensitive sensors that have achieved commercial success in DNA/RNA sequencing, with potential applications in protein sequencing and biomarker identification. Solid-state nanopores, in particular, face challenges such as instability and low signal-to-noise ratios (SNRs), which lead scientists to adopt data-driven methods for nanopore signal analysis, although data acquisition remains restrictive.
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