Discovering novelty in gene data: from sequential patterns to visualization

  • Authors:

  • Arnaud Sallaberry;Nicolas Pecheur;Sandra Bringay;Mathieu Roche;Maguelonne Teisseire

  • Affiliations:

  • LaBRI & INRIA Bordeaux - Sud Ouest & Pikko, France;LIRMM, Université Montpellier 2, France;LIRMM, Université Montpellier 3, France;LIRMM, Université Montpellier 2, France;CEMAGREF, UMR TETIS, France

  • Venue:
  • ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
  • Year:
  • 2010

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Abstract

Data mining techniques allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyse by end-users. In this paper, we focus on sequential pattern mining and propose a new visualization system, which aims at helping end-users to analyse extracted knowledge and to highlight the novelty according to referenced biological document databases. Our system is based on two visualization techniques: Clouds and solar systems. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimers disease and cancer.