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Back Cover: Deep Learning‐Enhanced Nanopore Sensing of Single‐Nanoparticle Translocation Dynamics (Small Methods 7/2021)
Author(s) -
Tsutsui Makusu,
Takaai Takayuki,
Yokota Kazumichi,
Kawai Tomoji,
Washio Takashi
Publication year - 2021
Publication title -
small methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.66
H-Index - 46
ISSN - 2366-9608
DOI - 10.1002/smtd.202170032
Subject(s) - nanopore , cover (algebra) , nanoscopic scale , nanotechnology , convolutional neural network , deep learning , ionic bonding , materials science , feature (linguistics) , artificial intelligence , computer science , nanopore sequencing , pattern recognition (psychology) , chemistry , engineering , ion , mechanical engineering , biochemistry , genome , gene , organic chemistry , linguistics , philosophy
In article number 2100191, Makusu Tsutsui, Tomoji Kawai, Takashi Washio, and co‐workers demonstrated the use of deep learning in nanopore sensing. Employing convolutional neural networks, ionic current was denoised in a high dimensional feature space that enabled to visualize hidden signals reflecting fast translocation motions of objects in a nanoscale conduit. This deep denoising paves a way for single molecule fingerprinting by ionic current using solid state nanopores.