title: Sequential patterns mining and gene sequence visualization to discover novelty from microarray data.
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.
authors.affiliation: LaBRI, INRIA Bordeaux Sud-Ouest, Pikko, 351, cours de la Libération, 33405 Talence Cedex, France. arnaud.sallaberry@labri.fr
authors.forename: A
authors.initials: A
authors.lastname: Sallaberry
authors.forename: N
authors.initials: N
authors.lastname: Pecheur
authors.forename: S
authors.initials: S
authors.lastname: Bringay
authors.forename: M
authors.initials: M
authors.lastname: Roche
authors.forename: M
authors.initials: M
authors.lastname: Teisseire
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D046228:Microarray Analysis / Q000379:methods*; D009369:Neoplasms / Q000235:genetics