GOPred: GO molecular function prediction by combined classifiers.
PLoS One
; 5(8): e12382, 2010 Aug 31.
Article
em En
| MEDLINE
| ID: mdl-20824206
ABSTRACT
Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http//kinaz.fen.bilkent.edu.tr/gopred).
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Biologia Computacional
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Turquia