QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.
dc.contributor.author | Misiunas, Karolis | |
dc.contributor.author | Ermann, Niklas | |
dc.contributor.author | Keyser, Ulrich | |
dc.date.accessioned | 2018-09-20T12:03:13Z | |
dc.date.available | 2018-09-20T12:03:13Z | |
dc.date.issued | 2018-06-13 | |
dc.identifier.issn | 1530-6984 | |
dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/280436 | |
dc.description.abstract | 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. | |
dc.format.medium | Print-Electronic | |
dc.language | eng | |
dc.publisher | American Chemical Society (ACS) | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing. | |
dc.type | Article | |
prism.endingPage | 4045 | |
prism.issueIdentifier | 6 | |
prism.publicationDate | 2018 | |
prism.publicationName | Nano Lett | |
prism.startingPage | 4040 | |
prism.volume | 18 | |
dc.identifier.doi | 10.17863/CAM.27807 | |
dcterms.dateAccepted | 2018-05-30 | |
rioxxterms.versionofrecord | 10.1021/acs.nanolett.8b01709 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018-06 | |
dc.contributor.orcid | Misiunas, Karolis [0000-0002-2236-6244] | |
dc.contributor.orcid | Ermann, Niklas [0000-0003-0280-8866] | |
dc.contributor.orcid | Keyser, Ulrich [0000-0003-3188-5414] | |
dc.identifier.eissn | 1530-6992 | |
dc.publisher.url | http://dx.doi.org/10.1021/acs.nanolett.8b01709 | |
rioxxterms.type | Journal Article/Review | |
pubs.funder-project-id | European Research Council (647144) | |
cam.issuedOnline | 2018-05-30 |
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