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Publikationstyp: Konferenz: Paper
Art der Begutachtung: Peer review (Publikation)
Titel: Typing plasmids with distributed sequence representation
Autor/-in: Kaufmann, Moritz
Schüle, Martin
Smits, Theo
Pothier, Joël
et. al: No
DOI: 10.1007/978-3-030-58309-5_16
10.21256/zhaw-20457
Tagungsband: Artificial Neural Networks in Pattern Recognition
Herausgeber/-in des übergeordneten Werkes: Schilling, Frank-Peter
Stadelmann, Thilo
Seite(n): 200
Seiten bis: 210
Angaben zur Konferenz: 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020
Erscheinungsdatum: 2-Sep-2020
Reihe: Lecture Notes in Computer Science
Reihenzählung: 12294
Verlag / Hrsg. Institution: Springer
Verlag / Hrsg. Institution: Cham
ISBN: 978-3-030-58308-8
978-3-030-58309-5
Sprache: Englisch
Schlagwörter: Word embedding; Support vector machine; Plasmid typing; Multidrug resistance; Antibiotic resistance
Fachgebiet (DDC): 572: Biochemie
Zusammenfassung: Multidrug 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/20457
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: Life Sciences und Facility Management
Organisationseinheit: Institut für Umwelt und Natürliche Ressourcen (IUNR)
Institut für Computational Life Sciences (ICLS)
Publiziert im Rahmen des ZHAW-Projekts: ChitinOMix – A multidisciplinary project to understand the effect of chitin soil amendment on the plant response, natural microbial community and the fate of human pathogenic bacteria
Enthalten in den Sammlungen:Publikationen Life Sciences und Facility Management

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Kaufmann, M., Schüle, M., Smits, T., & Pothier, J. (2020). Typing plasmids with distributed sequence representation [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 200–210). Springer. https://doi.org/10.1007/978-3-030-58309-5_16
Kaufmann, M. et al. (2020) ‘Typing plasmids with distributed sequence representation’, in F.-P. Schilling and T. Stadelmann (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 200–210. Available at: https://doi.org/10.1007/978-3-030-58309-5_16.
M. Kaufmann, M. Schüle, T. Smits, and J. Pothier, “Typing plasmids with distributed sequence representation,” in Artificial Neural Networks in Pattern Recognition, Sep. 2020, pp. 200–210. doi: 10.1007/978-3-030-58309-5_16.
KAUFMANN, Moritz, Martin SCHÜLE, Theo SMITS und Joël POTHIER, 2020. Typing plasmids with distributed sequence representation. In: Frank-Peter SCHILLING und Thilo STADELMANN (Hrsg.), Artificial Neural Networks in Pattern Recognition. Conference paper. Cham: Springer. 2 September 2020. S. 200–210. ISBN 978-3-030-58308-8
Kaufmann, Moritz, Martin Schüle, Theo Smits, and Joël Pothier. 2020. “Typing Plasmids with Distributed Sequence Representation.” Conference paper. In Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, 200–210. Cham: Springer. https://doi.org/10.1007/978-3-030-58309-5_16.
Kaufmann, Moritz, et al. “Typing Plasmids with Distributed Sequence Representation.” Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, Springer, 2020, pp. 200–10, https://doi.org/10.1007/978-3-030-58309-5_16.


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