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dc.contributor.authorMisiunas, Karolis
dc.contributor.authorErmann, Niklas
dc.contributor.authorKeyser, Ulrich
dc.date.accessioned2018-09-20T12:03:13Z
dc.date.available2018-09-20T12:03:13Z
dc.date.issued2018-06-13
dc.identifier.issn1530-6984
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280436
dc.description.abstractNanopore 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.mediumPrint-Electronic
dc.languageeng
dc.publisherAmerican Chemical Society (ACS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleQuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.
dc.typeArticle
prism.endingPage4045
prism.issueIdentifier6
prism.publicationDate2018
prism.publicationNameNano Lett
prism.startingPage4040
prism.volume18
dc.identifier.doi10.17863/CAM.27807
dcterms.dateAccepted2018-05-30
rioxxterms.versionofrecord10.1021/acs.nanolett.8b01709
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-06
dc.contributor.orcidMisiunas, Karolis [0000-0002-2236-6244]
dc.contributor.orcidErmann, Niklas [0000-0003-0280-8866]
dc.contributor.orcidKeyser, Ulrich [0000-0003-3188-5414]
dc.identifier.eissn1530-6992
dc.publisher.urlhttp://dx.doi.org/10.1021/acs.nanolett.8b01709
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Research Council (647144)
cam.issuedOnline2018-05-30


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International