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

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/.

published proceedings

  • PLoS One

altmetric score

  • 0.5

author list (cited authors)

  • Sahraeian, S., & Yoon, B.

citation count

  • 73

complete list of authors

  • Sahraeian, Sayed Mohammad Ebrahim||Yoon, Byung-Jun

editor list (cited editors)

  • Csermely, P.

publication date

  • July 2013