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

Authors:
A. Sallaberry;N. Pecheur;S. Bringay;M. Roche;M. Teisseire
Affiliations:
LaBRI, INRIA Bordeaux Sud-Ouest, Pikko, 351, cours de la Libération, 33405 Talence Cedex, France;LIRMM, Univ. Montpellier 2 - CNRS, 161 rue Ada 34095 Montpellier Cedex 5 France;LIRMM, Univ. Montpellier 2 - CNRS, 161 rue Ada 34095 Montpellier Cedex 5 France and MIAp Department, Univ. Montpellier 3, route de Mende 34199 Montpellier cedex 5, France;LIRMM, Univ. Montpellier 2 - CNRS, 161 rue Ada 34095 Montpellier Cedex 5 France;Cemagref, UMR TETIS, Maison de la teledetection, 500 rue Jean-François Breton, 34093 Montpellier, France
Venue:
Journal of Biomedical Informatics
Year:
2011

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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.