title: New methods for the analysis of binarized BIOLOG GN data of Vibrio species: minimization of stochastic complexity and cumulative classification.
abstract: 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.
authors.affiliation: Department of Mathematics, University of Turku, Finland. matsgyl@utu.fi
authors.forename: Mats
authors.initials: M
authors.lastname: Gyllenberg
authors.forename: Timo
authors.initials: T
authors.lastname: Koski
authors.forename: Peter
authors.initials: P
authors.lastname: Dawyndt
authors.forename: Tatu
authors.initials: T
authors.lastname: Lund
authors.forename: Fabiano
authors.initials: F
authors.lastname: Thompson
authors.forename: Brian
authors.initials: B
authors.lastname: Austin
authors.forename: Jean
authors.initials: J
authors.lastname: Swings
mesh_terms: D000465:Algorithms; D015373:Bacterial Typing Techniques; D002965:Classification / Q000379:methods*; D019295:Computational Biology; D013269:Stochastic Processes; D014733:Vibrio / Q000145:classification* / Q000235:genetics / Q000502:physiology