Research Article Open Access

Knowledge Discovery in Biochemical Pathways Using Minepathways

Ford Lumban Gaol1
  • 1 , Afganistan
Journal of Computer Science
Volume 6 No. 11, 2010, 1276-1282

DOI: https://doi.org/10.3844/jcssp.2010.1276.1282

Published On: 23 October 2010

How to Cite: Gaol, F. L. (2010). Knowledge Discovery in Biochemical Pathways Using Minepathways. Journal of Computer Science, 6(11), 1276-1282. https://doi.org/10.3844/jcssp.2010.1276.1282

Abstract

Problem statement: The advancement of the biochemical research gives profound effect to the collection of biochemical data. Approach: In the recent years, data and networks in biochemical pathways are abundant that allow to do process mining in order to obtain useful information. By using graph theory as a tool to model these interactions, it can be formally find the solution. Results: The core of the problem of mining patterns is a subgraph isomorphism which until now has been in the NP-class problems. Early identification showed that in the context biochemical pathways has unique node labeling that result simplifying pattern mining problem radically. Conclusion: Process will be more efficient because the end result that is needed is maximum pattern that could reduce redundant patterns. The algorithm that used is a modification of the maximum item set patterns that are empirically most efficiently at this time.

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Keywords

  • Biochemical pathways
  • graph theory
  • subgraph isomorphism
  • NP problems
  • maximum itemset pattern