Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures
Journal article, 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, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Malte Hermansson

University of Gothenburg


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

Vol. 22 5 517-522

Subject Categories

Other Biological Topics

Bioinformatics and Systems Biology



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