Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network.
J Theor Biol
; 430: 9-20, 2017 10 07.
Article
en En
| MEDLINE
| ID: mdl-28625475
ABSTRACT
Prediction of protein-protein interactions (PPIs) is of great significance. To achieve this, we propose a novel computational method for PPIs prediction based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins. Specifically, the physical and chemical properties of protein are the protein amino acid mutation rate and its hydrophobicity, respectively. The amino acid mutation rate is extracted using a BLOSUM62 matrix, which puts the protein sequence into block substitution matrix. The SNF model is exploited to fuse protein physical and chemical features of multiple data by iteratively updating each original network. Finally, the complementary features from the fused network are fed into a label propagation algorithm (LPA) for PPIs prediction. The experimental results show that the proposed method achieves promising performance and outperforms the traditional methods for the public dataset of H. pylori, Human, and Yeast. In addition, our proposed method achieves average accuracy of 76.65%, 81.98%, 84.56%, 84.01% and 84.38% on E. coli, C. elegans, H. sapien, H. pylori and M. musculus datasets, respectively. Comparison results demonstrate that the proposed method is very promising and provides a cost-effective alternative for predicting PPIs. The source code and all datasets are available at http//pan.baidu.com/s/1dF7rp7N.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Mapas de Interacción de Proteínas
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
J Theor Biol
Año:
2017
Tipo del documento:
Article
País de afiliación:
China