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Incremental Maintenance of Biological Databases Using Association Rule Mining

  • Kai-Tak Lam
  • Judice L. Y. Koh
  • Bharadwaj Veeravalli
  • Vladimir Brusic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)

Abstract

Biological research frequently requires specialist databases to support in-depth analysis about specific subjects. With the rapid growth of biological sequences in public domain data sources, it is difficult to keep these databases current with the sources. Simple queries formulated to retrieve relevant sequences typically return a large number of false matches and thus demanding manual filtration. In this paper, we propose a novel methodology that can support automatic incremental updating of specialist databases. Complex queries for incremental updating of relevant sequences are learned using Association Rule Mining (ARM), resulting in a significant reduction in false positive matches. This is the first time ARM is used in formulating descriptive queries for the purpose of incremental maintenance of specialised biological databases. We have implemented and tested our methodology on two real-world databases. Our experiments conclusively show that the methodology guarantees an F-score of up to 80% in detecting new sequences for these two databases.

Keywords

Frequent Itemsets Association Rule Mining Complex Query Specialist Database Original Query 
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

  • Kai-Tak Lam
    • 1
  • Judice L. Y. Koh
    • 2
    • 3
  • Bharadwaj Veeravalli
    • 1
  • Vladimir Brusic
    • 4
  1. 1.Department of Electrical & Computer EngineeringNational University of SingaporeSingapore
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.School of ComputingNational University of SingaporeSingapore
  4. 4.Australian Centre for Plant Functional Genomics, School of Land and Food Sciences, and the Institute for Molecular BioscienceUniversity of QueenslandBrisbaneAustralia

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