Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network Article Swipe

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Darío Demattíes , Chenyu Wen , Mauricio D. Pérez , Dian Zhou , Shi‐Li Zhang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1021/acsnano.1c03842

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

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Nanopore Computer science Artificial intelligence Feature extraction Noise (video) Path (computing) Pattern recognition (psychology) SIGNAL (programming language) Waveform Pulse (music) Amplitude Algorithm Radar Physics Materials science Nanotechnology Telecommunications Detector Quantum mechanics Image (mathematics) Programming language

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Type: article
Language: en
Landing Page: https://doi.org/10.1021/acsnano.1c03842
PDF: https://pubs.acs.org/doi/pdf/10.1021/acsnano.1c03842
OA Status: hybrid
Cited By: 26
References: 40
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DOI: https://doi.org/10.1021/acsnano.1c03842

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Title: Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network

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Language: en

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Publication year: 2021

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Publication date: 2021-08-17

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Authors: Darío Demattíes, Chenyu Wen, Mauricio D. Pérez, Dian Zhou, Shi‐Li Zhang

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Landing page: https://doi.org/10.1021/acsnano.1c03842

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Concepts: Nanopore, Computer science, Artificial intelligence, Feature extraction, Noise (video), Path (computing), Pattern recognition (psychology), SIGNAL (programming language), Waveform, Pulse (music), Amplitude, Algorithm, Radar, Physics, Materials science, Nanotechnology, Telecommunications, Detector, Quantum mechanics, Image (mathematics), Programming language

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Cited by: 26

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