Towards Enhanced Retrieval of Biological Models through Annotation-Based Ranking

  • Authors:

  • Dagmar Köhn;Carsten Maus;Ron Henkel;Martin Kolbe

  • Affiliations:

  • Institute of Computer Science, Database and Information Systems Group, University of Rostock, Rostock, Germany 18059;Institute of Computer Science, Modelling and Simulation Group, University of Rostock, Rostock, Germany 18059;Institute of Computer Science, Database and Information Systems Group, University of Rostock, Rostock, Germany 18059;Institute of Computer Science, Database and Information Systems Group, University of Rostock, Rostock, Germany 18059

  • Venue:
  • DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
  • Year:
  • 2009

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Abstract

Modelling and simulation methods gain increasing importance for the understanding of biological systems. The growing number of available computational models makes support in maintenance and retrieval of those models essential to the community. This article discusses which model information are helpful for efficient retrieval and how existing similarity measures and ranking techniques can be used to enhance the retrieval process, i. e. the model reuse. With the development of new tools and modelling formalisms, there also is an increasing demand for performing search independent of the models' encoding. Therefore, the presented approach is not restricted to certain model storage formats. Instead, the model meta-information is used for retrieval and ranking of the search result. Meta-information include general information about the model, its encoded species and reactions, but also information about the model behaviour and related simulation experiment descriptions.