SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks.
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 furt...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2013-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3710069?pdf=render |
id |
doaj-91ee0c563080486fa6887271a1c27daf |
---|---|
record_format |
Article |
spelling |
doaj-91ee0c563080486fa6887271a1c27daf2020-11-25T01:19:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0187e6799510.1371/journal.pone.0067995SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks.Sayed Mohammad Ebrahim SahraeianByung-Jun YoonIn 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/.http://europepmc.org/articles/PMC3710069?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sayed Mohammad Ebrahim Sahraeian Byung-Jun Yoon |
spellingShingle |
Sayed Mohammad Ebrahim Sahraeian Byung-Jun Yoon SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. PLoS ONE |
author_facet |
Sayed Mohammad Ebrahim Sahraeian Byung-Jun Yoon |
author_sort |
Sayed Mohammad Ebrahim Sahraeian |
title |
SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
title_short |
SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
title_full |
SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
title_fullStr |
SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
title_full_unstemmed |
SMETANA: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
title_sort |
smetana: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
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/. |
url |
http://europepmc.org/articles/PMC3710069?pdf=render |
work_keys_str_mv |
AT sayedmohammadebrahimsahraeian smetanaaccurateandscalablealgorithmforprobabilisticalignmentoflargescalebiologicalnetworks AT byungjunyoon smetanaaccurateandscalablealgorithmforprobabilisticalignmentoflargescalebiologicalnetworks |
_version_ |
1725139165949984768 |