Document Detail


RESQUE: Network Reduction Using Semi-Markov Random Walk Scores for Efficient Querying of Biological Networks.
MedLine Citation:
PMID:  22730436     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
MOTIVATION: Recent technological advances in measuring molecular interactions have resulted in an increasing number of large-scale biological networks. Translation of these enormous network data into meaningful biological insights requires efficient computational techniques that can unearth the biological information that is encoded in the networks. One such example is network querying, which aims to identify similar subnetwork regions in a large target network that are similar to a given query network. Network querying tools can be used to identify novel biological pathways that are homologous to known pathways, thereby enabling knowledge transfer across different organisms. RESULTS: In this paper, we introduce an efficient algorithm for querying large-scale biological networks, called RESQUE. The proposed algorithm adopts a semi-Markov random walk model to probabilistically estimate the correspondence scores between nodes that belong to different networks. The target network is iteratively reduced based on the estimated correspondence scores, which are also iteratively re-estimated to improve accuracy until the best matching subnet-work emerges. We demonstrate that the proposed network querying scheme is computationally efficient, can handle any network query with an arbitrary topology, and yields accurate querying results. AVAILABILITY: The source code of RESQUE is freely available at http://www.ece.tamu.edu/~bjyoon/RESQUE/ CONTACT: bjyoon@ece.tamu.edu.
Authors:
Sayed Mohammad Ebrahim Sahraeian; Byung-Jun Yoon
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-6-23
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  -     ISSN:  1367-4811     ISO Abbreviation:  -     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-6-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
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