A probabilistic learning approach to whole-genome operon prediction.
Proc Int Conf Intell Syst Mol Biol
; 8: 116-27, 2000.
Article
em En
| MEDLINE
| ID: mdl-10977072
ABSTRACT
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this task from a rich variety of data types including sequence data, gene expression data, and functional annotations associated with genes. We use multiple learned models that individually predict promoters, terminators and operons themselves. A key part of our approach is a dynamic programming method that uses our predictions to map every known and putative gene in a given genome into its most probable operon. We evaluate our approach using data from the E. coli K-12 genome.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Óperon
/
Genoma Bacteriano
/
Modelos Genéticos
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Proc Int Conf Intell Syst Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
BIOTECNOLOGIA
Ano de publicação:
2000
Tipo de documento:
Article
País de afiliação:
Estados Unidos