CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures

Daniel Dalevi (Institutionen för data- och informationsteknik, Datavetenskap, Bioinformatik (Chalmers)) ; Devdatt Dubhashi (Institutionen för data- och informationsteknik, Datavetenskap, Bioinformatik (Chalmers)) ; Malte Hermansson
Bioinformatics (1367-4803). Vol. 22 (2006), 5, p. 517-522.
[Artikel, refereegranskad vetenskaplig]

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).



Denna post skapades 2007-02-23. Senast ändrad 2008-02-21.
CPL Pubid: 37550

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för data- och informationsteknik, Datavetenskap, Bioinformatik (Chalmers)
Institutionen för cell- och molekylärbiologi (1994-2011)

Ämnesområden

Bioinformatik och systembiologi
Annan biologi

Chalmers infrastruktur