Prediction of Protein-Protein Interactions by Evidence Combining Methods.
Int J Mol Sci
; 17(11)2016 Nov 22.
Article
en En
| MEDLINE
| ID: mdl-27879651
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Proteínas
/
Biología Computacional
/
Mapeo de Interacción de Proteínas
/
Minería de Datos
/
Máquina de Vectores de Soporte
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Int J Mol Sci
Año:
2016
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza