A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks.
BMC Bioinformatics
; 18(Suppl 13): 463, 2017 Dec 01.
Article
em En
| MEDLINE
| ID: mdl-29219066
ABSTRACT
BACKGROUND:
The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.RESULTS:
In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.CONCLUSIONS:
In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Saccharomyces cerevisiae
/
Algoritmos
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Biologia Computacional
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Proteínas de Saccharomyces cerevisiae
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Mapeamento de Interação de Proteínas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
China