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New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification
2002 (English)In: Systematic and Applied Microbiology, ISSN 0723-2020, Vol. 25, no 3, 403-415 p.Article in journal (Refereed) Published
Abstract [en]

We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

Place, publisher, year, edition, pages
2002. Vol. 25, no 3, 403-415 p.
Keyword [en]
bacterial taxonomy, machine learning, cumulative classification, ribosomal-rna sequences, identification, taxonomy
Identifiers
URN: urn:nbn:se:kth:diva-21998ISI: 000178865500010OAI: oai:DiVA.org:kth-21998DiVA: diva2:340696
Note
QC 20100525Available from: 2010-08-10 Created: 2010-08-10Bibliographically approved

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Koski, Timo

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