Deep Learning‐Enhanced Nanopore Sensing of Single‐Nanoparticle Translocation Dynamics
Makusu Tsutsui, Takayuki Takaai, Kazumichi Yokota, Tomoji Kawai, Takashi Washio
Abstract
Noise is ubiquitous in real space that hinders detection of minute yet important signals in electrical sensors. Here, the authors report on a deep learning approach for denoising ionic current in resistive pulse sensing. Electrophoretically-driven translocation motions of single-nanoparticles in a nano-corrugated nanopore are detected. The noise is reduced by a convolutional auto-encoding neural network, designed to iteratively compare and minimize differences between a pair of waveforms via a gradient descent optimization. This denoising in a high-dimensional feature space is demonstrated to allow detection of the corrugation-derived wavy signals that cannot be identified in the raw curves nor after digital processing in frequency domains under the given noise floor, thereby enabled in-situ tracking to electrokinetic analysis of fast-moving single- and double-nanoparticles. The ability of the unlabeled learning to remove noise without compromising temporal resolution may be useful in solid-state nanopore sensing of protein structure and polynucleotide sequence.