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

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Abstract

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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APA

Dematties, D., Wen, C., Pérez, M. D., Zhou, D., & Zhang, S. L. (2021). Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network. ACS Nano, 15(9), 14419–14429. https://doi.org/10.1021/acsnano.1c03842

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