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/10.48550/arxiv.2105.03660

Temporary 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 performance is evaluated on generated datasets and further applied to experimental data of DNA and protein translocation. The B-Net results show remarkably small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to one, an impossibility for threshold-based algorithms. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.

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

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2105.03660
PDF: https://arxiv.org/pdf/2105.03660
OA Status: green
References: 36
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2105.03660

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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: 2021-05-08

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

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

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abstract_inverted_index.based	57
abstract_inverted_index.equal	129
abstract_inverted_index.noisy	47
abstract_inverted_index.pulse	76
abstract_inverted_index.ratio	128
abstract_inverted_index.shown	120
abstract_inverted_index.small	110
abstract_inverted_index.beyond	145
abstract_inverted_index.caused	9
abstract_inverted_index.errors	112
abstract_inverted_index.priori	83
abstract_inverted_index.pulsed	146
abstract_inverted_index.pulses	20, 44, 70
abstract_inverted_index.sensor	8
abstract_inverted_index.single	41
abstract_inverted_index.stable	114
abstract_inverted_index.target	12
abstract_inverted_index.ability	73
abstract_inverted_index.applied	96
abstract_inverted_index.because	34
abstract_inverted_index.bi-path	60
abstract_inverted_index.capable	121
abstract_inverted_index.changes	1
abstract_inverted_index.current	22
abstract_inverted_index.feature	27, 55, 79
abstract_inverted_index.further	95, 119
abstract_inverted_index.generic	141
abstract_inverted_index.network	61
abstract_inverted_index.protein	103
abstract_inverted_index.results	107
abstract_inverted_index.signals	31, 144
abstract_inverted_index.traces.	23
abstract_inverted_index.trends.	115
abstract_inverted_index.without	81
abstract_inverted_index.(B-Net).	62
abstract_inverted_index.acquires	67
abstract_inverted_index.analytes	13
abstract_inverted_index.assigned	84
abstract_inverted_index.datasets	93
abstract_inverted_index.learning	53
abstract_inverted_index.nanopore	7, 147
abstract_inverted_index.recorded	15
abstract_inverted_index.relative	111
abstract_inverted_index.sequence	18
abstract_inverted_index.Prevalent	24
abstract_inverted_index.Temporary	0
abstract_inverted_index.amplitude	36
abstract_inverted_index.currents.	148
abstract_inverted_index.developed	138
abstract_inverted_index.empirical	35
abstract_inverted_index.evaluated	90
abstract_inverted_index.generated	92
abstract_inverted_index.training,	64
abstract_inverted_index.algorithms	25
abstract_inverted_index.electrical	3
abstract_inverted_index.extraction	28, 56, 80
abstract_inverted_index.processing	123
abstract_inverted_index.pulse-like	30, 143
abstract_inverted_index.remarkably	109
abstract_inverted_index.resistance	4
abstract_inverted_index.thresholds	37
abstract_inverted_index.algorithms.	136
abstract_inverted_index.background.	48
abstract_inverted_index.objectivity	33
abstract_inverted_index.parameters.	85
abstract_inverted_index.performance	88
abstract_inverted_index.recognition	77
abstract_inverted_index.experimental	98
abstract_inverted_index.prototypical	69
abstract_inverted_index.user-defined	39
abstract_inverted_index.impossibility	133
abstract_inverted_index.translocating	11
abstract_inverted_index.translocation.	104
abstract_inverted_index.signal-to-noise	127
abstract_inverted_index.threshold-based	135
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile