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Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Sliding window discretization: a new method for multiple band matching of bacterial genotyping fingerprints
Authors: Austin, Brian
Dawyndt, Peter
Gyllenberg, Mats
Koski, Timo
Lund, Tatu
Swings, Jean
Thompson, Fabiano L
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Issue Date: Nov-2004
Publisher: Springer
Citation: Austin B, Dawyndt P, Gyllenberg M, Koski T, Lund T, Swings J & Thompson FL (2004) Sliding window discretization: a new method for multiple band matching of bacterial genotyping fingerprints, Bulletin of Mathematical Biology, 66 (6), pp. 1575-1596.
Abstract: Microbiologists have traditionally applied hierarchical clustering algorithms as their mathematical tool of choice to unravel the taxonomic relationships between micro-organisms. However, the interpretation of such hierarchical classifications suffers from being subjective, in that a variety of ad hoc choices must be made during their construction. On the other hand, the application of more profound and objective mathematical methods—such as the minimization of stochastic complexity—for the classification of bacterial genotyping fingerprints data is hampered by the prerequisite that such methods only act upon vectorized data. In this paper we introduce a new method, coined sliding window discretization, for the transformation of genotypic fingerprint patterns into binary vector format. In the context of an extensive amplified fragment length polymorphism (AFLP) data set of 507 strains from the Vibrionaceae family that has previously been analysed, we demonstrate by comparison with a number of other discretization methods that this new discretization method results in minimal loss of the original information content captured in the banding patterns. Finally, we investigate the implications of the different discretization methods on the classification of bacterial genotyping fingerprints by minimization of stochastic complexity, as it is implemented in the BinClass software package for probabilistic clustering of binary vectors. The new taxonomic insights learned from the resulting classification of the AFLP patterns will prove the value of combining sliding window discretization with minimization of stochastic complexity, as an alternative classification algorithm for bacterial genotyping fingerpri
Type: Journal Article
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Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Heriot-Watt University
University of Turku
Linkoping University
Nokia Corporation, Finland
Ghent University
Ghent University

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