Mining Substructures in Protein Data

  • Authors:

  • Fedja Hadzic;Tharam S. Dillon;Amandeep S. Sidhu;Elizabeth Chang;Henry Tan

  • Affiliations:

  • University of Technology Sydney, Australia;University of Technology Sydney, Australia;University of Technology Sydney, Australia;Curtin University of Technology Perth, Australia;University of Technology Sydney, Australia

  • Venue:
  • ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
  • Year:
  • 2006

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Abstract

In this paper we consider the "Prions' database that describes protein instances stored for Human Prion Proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data.