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Publication type: Conference paper
Type of review: Peer review (publication)
Title: Typing plasmids with distributed sequence representation
Authors: Kaufmann, Moritz
Schüle, Martin
Smits, Theo
Pothier, Joël
et. al: No
DOI: 10.1007/978-3-030-58309-5_16
Proceedings: Artificial Neural Networks in Pattern Recognition
Editors of the parent work: Schilling, Frank-Peter
Stadelmann, Thilo
Page(s): 200
Pages to: 210
Conference details: 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020
Issue Date: 2-Sep-2020
Series: Lecture Notes in Computer Science
Series volume: 12294
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-58308-8
Language: English
Subjects: Word embedding; Support vector machine; Plasmid typing; Multidrug resistance; Antibiotic resistance
Subject (DDC): 572: Biochemistry
Abstract: 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.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Natural Resource Sciences (IUNR)
Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: 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
Appears in collections:Publikationen Life Sciences und Facility Management

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