QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.
American Chemical Society (ACS)
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Misiunas, K., Ermann, N., & Keyser, U. (2018). QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.. Nano letters, 18 (6), 4040-4045. https://doi.org/10.1021/acs.nanolett.8b01709
Nanopore sensing is a versatile technique for the analysis of molecules on the single-molecule level. However, extracting information from data with established algorithms usually requires time-consuming checks by an experienced researcher due to inherent variability of solid-state nanopores. Here, we develop a convolutional neural network (CNN) for the fully automated extraction of information from the time-series signals obtained by nanopore sensors. In our demonstration, we use a previously published dataset on multiplexed single-molecule protein sensing. The neural network learns to classify translocation events with greater accuracy than previously possible, while also increasing the number of analysable events by a factor of five. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.
ECH2020 EUROPEAN RESEARCH COUNCIL (ERC) (647144)
External DOI: https://doi.org/10.1021/acs.nanolett.8b01709
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280436
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/