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