A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.
Molecules
; 22(12)2017 Dec 08.
Article
en En
| MEDLINE
| ID: mdl-29292776
ABSTRACT
Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas de Saccharomyces cerevisiae
/
Mapeo de Interacción de Proteínas
/
Modelos Biológicos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Molecules
Asunto de la revista:
BIOLOGIA
Año:
2017
Tipo del documento:
Article
País de afiliación:
China