Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20457
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dc.contributor.authorKaufmann, Moritz-
dc.contributor.authorSchüle, Martin-
dc.contributor.authorSmits, Theo-
dc.contributor.authorPothier, Joël-
dc.date.accessioned2020-09-10T16:51:44Z-
dc.date.available2020-09-10T16:51:44Z-
dc.date.issued2020-09-02-
dc.identifier.isbn978-3-030-58308-8de_CH
dc.identifier.isbn978-3-030-58309-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20457-
dc.description.abstractMultidrug resistant bacteria represent an increasing challenge for medicine. In bacteria, most antibiotic resistances are transmitted by plasmids. Therefore, it is important to study the spread of plasmids in detail in order to initiate possible countermeasures. The classification of plasmids can provide insights into the epidemiology and transmission of plasmid-mediated antibiotic resistance. The previous methods to classify plasmids are replicon typing and MOB typing. Both methods are time consuming and labor-intensive. Therefore, a new approach to plasmid typing was developed, which uses word embeddings and support vector machines (SVM) to simplify plasmid typing. Visualizing the word embeddings with t-distributed stochastic neighbor embedding (t-SNE) shows that the word embeddings finds distinct structure in the plasmid sequences. The SVM assigned the plasmids in the testing dataset with an average accuracy of 85.9% to the correct MOB type.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectWord embeddingde_CH
dc.subjectSupport vector machinede_CH
dc.subjectPlasmid typingde_CH
dc.subjectMultidrug resistancede_CH
dc.subjectAntibiotic resistancede_CH
dc.subject.ddc572: Biochemiede_CH
dc.titleTyping plasmids with distributed sequence representationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Umwelt und Natürliche Ressourcen (IUNR)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-58309-5_16de_CH
dc.identifier.doi10.21256/zhaw-20457-
zhaw.conference.details9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end210de_CH
zhaw.pages.start200de_CH
zhaw.parentwork.editorSchilling, Frank-Peter-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number12294de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.funding.snf189340de_CH
zhaw.webfeedUmweltgenomikde_CH
zhaw.funding.zhawChitinOMix – A multidisciplinary project to understand the effect of chitin soil amendment on the plant response, natural microbial community and the fate of human pathogenic bacteriade_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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