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Using Term Lists and Inverted Files to Improve Search Speed for Metabolic Pathway Databases

  • Greeshma Neglur
  • Robert L. Grossman
  • Natalia Maltsev
  • Clement Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4075)

Abstract

This paper describes a technique for efficiently searching metabolic pathways similar to a given query pathway, from a pathway database. Metabolic pathways can be converted into labeled directed graphs where the nodes represent chemical compounds. Similarity between two graphs can be computed using a metric based on Maximal Common Subgraph (MCS). By maintaining an inverted file that indexes all pathways in a database on their edges, our algorithm finds and ranks all pathways similar to the user input query pathway in time, which is linear in the total number of occurrences of the edges in common with the query in the entire database.

Keywords

Common Edge Label Graph Query Graph Adjacency List Quotient Graph 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Greeshma Neglur
    • 1
  • Robert L. Grossman
    • 1
  • Natalia Maltsev
    • 2
  • Clement Yu
    • 3
  1. 1.Laboratory for Advanced ComputingUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Argonne National LaboratoryMath and Computer Science DivisionArgonneUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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