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A Method for Similarity-Based Grouping of Biological Data

  • Vaida Jakonienė
  • David Rundqvist
  • Patrick Lambrix
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4075)

Abstract

Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results.

Keywords

Gene Ontology Mutual Information Data Entry Biological Data Semantic Similarity 
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

  • Vaida Jakonienė
    • 1
  • David Rundqvist
    • 1
  • Patrick Lambrix
    • 1
  1. 1.Department of Computer and Information ScienceLinköpings universitetLinköpingSweden

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