Open AccessA Bayesian network approach to operon prediction
Author(s) -
Joseph Bockhorst,
Mark Craven,
David Page,
Jude Shavlik,
Jeremy D. Glasner
Publication year - 2003
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btg147
Subject(s) - bayesian probability , computer science , operon , bayesian network , computational biology , artificial intelligence , chemistry , biology , gene , biochemistry , escherichia coli
In order to understand transcription regulation in a given prokaryotic genome, it is critical to identify operons, the fundamental units of transcription, in such species. While there are a growing number of organisms whose sequence and gene coordinates are known, by and large their operons are not known.
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