Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

Arnaud Sallaberry 1, 2 Nicolas Pecheur 3 Sandra Bringay 2 Mathieu Roche 4 Maguelonne Teisseire 5, 2
1 GRAVITE - Graph Visualization and Interactive Exploration
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Data mining 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 analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. 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 Alzheimer's disease and cancer.
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https://hal.archives-ouvertes.fr/hal-00625539
Contributor : Arnaud Sallaberry <>
Submitted on : Wednesday, September 21, 2011 - 7:24:32 PM
Last modification on : Wednesday, September 18, 2019 - 4:04:04 PM

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Arnaud Sallaberry, Nicolas Pecheur, Sandra Bringay, Mathieu Roche, Maguelonne Teisseire. Sequential patterns mining and gene sequence visualization to discover novelty from microarray data. Journal of Biomedical Informatics, Elsevier, 2011, 44 (5), pp.760-774. ⟨10.1016/j.jbi.2011.04.002⟩. ⟨hal-00625539⟩

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