A specialized learner for inferring structured cis-regulatory modules.
BMC Bioinformatics
; 7: 528, 2006 Dec 05.
Article
en En
| MEDLINE
| ID: mdl-17147812
ABSTRACT
BACKGROUND:
The process of transcription is controlled by systems of transcription factors, which bind to specific patterns of binding sites in the transcriptional control regions of genes, called cis-regulatory modules (CRMs). We present an expressive and easily comprehensible CRM representation which is capable of capturing several aspects of a CRM's structure and distinguishing between DNA sequences which do or do not contain it. We also present a learning algorithm tailored for this domain, and a novel method to avoid overfitting by controlling the expressivity of the model.RESULTS:
We are able to find statistically significant CRMs more often then a current state-of-the-art approach on the same data sets. We also show experimentally that each aspect of our expressive CRM model space makes a positive contribution to the learned models on yeast and fly data.CONCLUSION:
Structural aspects are an important part of CRMs, both in terms of interpreting them biologically and learning them accurately. Source code for our algorithm is available at http//www.cs.wisc.edu/~noto/crm.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Factores de Transcripción
/
Transcripción Genética
/
Reconocimiento de Normas Patrones Automatizadas
/
Inteligencia Artificial
/
Secuencias Reguladoras de Ácidos Nucleicos
/
Análisis de Secuencia de ADN
/
Elementos Reguladores de la Transcripción
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2006
Tipo del documento:
Article
País de afiliación:
Estados Unidos