Advertisement

RESQUE: Network Reduction Using Semi-Markov Random Walk Scores for Efficient Querying of Biological Networks (Extended Abstract)

  • Sayed Mohammad Ebrahim Sahraeian
  • Byung-Jun Yoon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7262)

Abstract

In this work, we present RESQUE, an efficient algorithm for querying large-scale biological networks. The algorithm uses a semi-Markov random walk model to estimate the correspondence scores between nodes across different networks. The target network is iteratively reduced based on the node correspondence scores, which are also iteratively re-estimated for improved accuracy, until the best matching subnetwork emerges. The proposed network querying scheme is computationally efficient, can handle any network query with arbitrary topology, and yields accurate querying results.

Keywords

Biological Network Protein Interaction Network Target Network Arbitrary Topology Query Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bruckner, S., Huffner, F., Karp, R.M., Shamir, R., Sharan, R.: Topology-free querying of protein interaction networks. J. Comput. Biol. 17, 237–252 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Dost, B., Shlomi, T., Gupta, N., Ruppin, E., Bafna, V., Sharan, R.: QNet: A tool for querying protein interaction networks. J. Comput. Biol. 15(7), 913–925 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24, 427–433 (2006)CrossRefGoogle Scholar
  4. 4.
    Yang, Q., Sze, S.: Path matching and graph matching in biological networks. J. Comput. Biol. 14, 56–67 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sayed Mohammad Ebrahim Sahraeian
    • 1
  • Byung-Jun Yoon
    • 1
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA

Personalised recommendations