SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks.
PLoS One
; 8(7): e67995, 2013.
Article
en En
| MEDLINE
| ID: mdl-23874484
ABSTRACT
In this paper we introduce an efficient algorithm for alignment of multiple large-scale biological networks. In this scheme, we first compute a probabilistic similarity measure between nodes that belong to different networks using a semi-Markov random walk model. The estimated probabilities are further enhanced by incorporating the local and the cross-species network similarity information through the use of two different types of probabilistic consistency transformations. The transformed alignment probabilities are used to predict the alignment of multiple networks based on a greedy approach. We demonstrate that the proposed algorithm, called SMETANA, outperforms many state-of-the-art network alignment techniques, in terms of computational efficiency, alignment accuracy, and scalability. Our experiments show that SMETANA can easily align tens of genome-scale networks with thousands of nodes on a personal computer without any difficulty. The source code of SMETANA is available upon request. The source code of SMETANA can be downloaded from http//www.ece.tamu.edu/~bjyoon/SMETANA/.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Probabilidad
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Biología Computacional
/
Mapas de Interacción de Proteínas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2013
Tipo del documento:
Article
País de afiliación:
Estados Unidos