QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.
Keyser, Ulrich F
American Chemical Society (ACS)
MetadataShow full item record
Misiunas, K., Ermann, N., & Keyser, U. F. (2018). QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.. Nano Lett, 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 data set 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 analyzable events by a factor of 5. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.
q-bio.QM, q-bio.QM, physics.data-an, J.2; I.2.6; I.5.1
European Research Council (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/
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