SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks.

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.

SUBMITTER: Sahraeian SM 

PROVIDER: S-EPMC3710069 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature