Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects.
Bioinformatics
; 23(2): e156-62, 2007 Jan 15.
Article
en En
| MEDLINE
| ID: mdl-17237085
ABSTRACT
MOTIVATION The process of transcription is controlled by systems of factors which bind in specific arrangements, called cis-regulatory modules (CRMs), in promoter regions. We present a discriminative learning algorithm which simultaneously learns the DNA binding site motifs as well as the logical structure and spatial aspects of CRMs. RESULTS:
Our results on yeast datasets show better predictive accuracy than a current state-of-the-art approach on the same datasets. Our results on yeast, fly and human datasets show that the inclusion of logical and spatial aspects improves the predictive accuracy of our learned models.AVAILABILITY:
Source code is available at http//www.cs.wisc.edu/~noto/crm
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Factores de Transcripción
/
Algoritmos
/
Mapeo Cromosómico
/
Genoma Fúngico
/
Análisis de Secuencia de ADN
/
Elementos Reguladores de la Transcripción
/
Modelos Genéticos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2007
Tipo del documento:
Article
País de afiliación:
Estados Unidos