SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks |
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Authors: | Sayed Mohammad Ebrahim Sahraeian Byung-Jun Yoon |
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Institution: | 1. Department of Plant and Microbial Biology, University of California, Berkely, California, United States of America.; 2. Department of Electrical and Computer Engineering, Texas A & M University, College Station, Texas, United States of America.; Semmelweis University, Hungary, |
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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/. |
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