Deep Learning-Enhanced Nanopore Sensing of Single-Nanoparticle Translocation Dynamics.
Small Methods
; 5(7): e2100191, 2021 07.
Article
en En
| MEDLINE
| ID: mdl-34928002
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Nanopartículas
/
Nanoporos
/
Aprendizaje Profundo
Idioma:
En
Revista:
Small Methods
Año:
2021
Tipo del documento:
Article
País de afiliación:
Japón