Splice site prediction using stochastic regular grammars
Genet. mol. res. (Online)
;
6(1): 105-115, 2007. tab, ilus
Artículo
en Inglés
| LILACS
| ID: lil-456755
ABSTRACT
This paper presents a novel approach to the problem of splice site prediction, by applying stochastic grammar inference. We used four grammar inference algorithms to infer 1465 grammars, and used 10-fold cross-validation to select the best grammar for each algorithm. The corresponding grammars were embedded into a classifier and used to run splice site prediction and compare the results with those of NNSPLICE, the predictor used by the Genie gene finder. We indicate possible paths to improve this performance by using Sakakibaras windowing technique to find probability thresholds that will lower false-positive predictions.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Algoritmos
/
Inteligencia Artificial
/
Modelos Moleculares
/
Empalme del ARN
/
Procesos Estocásticos
Tipo de estudio:
Estudio pronóstico
/
Factores de riesgo
Límite:
Humanos
Idioma:
Inglés
Revista:
Genet. mol. res. (Online)
Asunto de la revista:
Biologia Molecular
/
Genética
Año:
2007
Tipo del documento:
Artículo
País de afiliación:
Brasil
Institución/País de afiliación:
Bolsa de Mercadorias e Futuros/BR
/
Universidade de São Paulo/BR
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