Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures
Artikel i vetenskaplig tidskrift, 2006

Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in Neisseria meningitis. In this study we extend the classifier approach using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk).


Daniel Dalevi

Chalmers, Data- och informationsteknik, Datavetenskap

Devdatt Dubhashi

Chalmers, Data- och informationsteknik, Datavetenskap

Malte Hermansson

Göteborgs universitet


1367-4803 (ISSN) 1367-4811 (eISSN)

Vol. 22 517-522


Annan biologi

Bioinformatik och systembiologi