A multi-objective evolutionary conceptual clustering methodology for gene annotation within structural databases: A case of study on the Gene Ontology database

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@Article{Romero-Zaliz:2009:ieeeTEC,
  author =       "Rocio C. Romero-Zaliz and Cristina Rubio-Escudero and 
                 J. Perren Cobb and Francisco Herrera and 
                 Oscar Cordon and Igor Zwir",
  title =        "A multi-objective evolutionary conceptual clustering
                 methodology for gene annotation within structural
                 databases: A case of study on the Gene Ontology
                 database",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2008",
  volume =       "12",
  number =       "6",
  month =        dec,
  pages =        "679--701",
  URL =          "http://sci2s.ugr.es/publications/ficheros/2008-IEEE_TEC%20-%20Romero-Zaliz-%20Multiobjective%20Evolutionary%20Conceptual%20Clustering.pdf",
  DOI =          "doi:10.1109/TEVC.2008.915995",
  ISSN =         "1089-778X",
  keywords =     "genetic algorithms, genetic programming, Conceptual
                 clustering, database annotation, evolutionary
                 algorithms, gene expression profiles, gene ontology,
                 knowledge discovery, multiobjective optimization",
  abstract =     "Current tools and techniques devoted to examine the
                 content of large databases are often hampered by their
                 inability to support searches based on criteria that
                 are meaningful to their users. These shortcomings are
                 particularly evident in data banks storing
                 representations of structural data such as biological
                 networks. Conceptual clustering techniques have
                 demonstrated to be appropriate for uncovering
                 relationships between features that characterize
                 objects in structural data. However, typical conceptual
                 clustering approaches normally recover the most obvious
                 relations, but fail to discover the less frequent but
                 more informative underlying data associations. The
                 combination of evolutionary algorithms with
                 multiobjective and multimodal optimization techniques
                 constitutes a suitable tool for solving this problem.
                 We propose a novel conceptual clustering methodology
                 termed evolutionary multiobjective conceptual
                 clustering (EMO-CC), relying on the NSGA-II
                 multiobjective (MO) genetic algorithm. We apply this
                 methodology to identify conceptual models in structural
                 databases generated from gene ontologies. These models
                 can explain and predict phenotypes in the
                 immunoinflammatory response problem, similar to those
                 provided by gene expression or other genetic markers.
                 The analysis of these results reveals that our approach
                 uncovers cohesive clusters, even those comprising a
                 small number of observations explained by several
                 features, which allows describing objects and their
                 interactions from different perspectives and at
                 different levels of detail.",
  notes =        "also known as \cite{4469888}",
}

Genetic Programming entries for Rocio C Romero Zalizo Cristina Rubio Escudero J Perren Cobb Francisco Herrera Oscar Cordon Igor Zwir

Citations