A multi-objective evolutionary conceptual clustering methodology for gene annotation within structural databases: A case of study on the Gene Ontology database
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Romero-Zaliz:2009:ieeeTEC,
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author = "Rocio C. Romero-Zaliz and Cristina Rubio-Escudero and
J. Perren Cobb and Francisco Herrera and
Oscar Cordon and Igor Zwir",
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title = "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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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2008",
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volume = "12",
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number = "6",
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month = dec,
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pages = "679--701",
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URL = "http://sci2s.ugr.es/publications/ficheros/2008-IEEE_TEC%20-%20Romero-Zaliz-%20Multiobjective%20Evolutionary%20Conceptual%20Clustering.pdf",
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DOI = "doi:10.1109/TEVC.2008.915995",
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ISSN = "1089-778X",
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keywords = "genetic algorithms, genetic programming, Conceptual
clustering, database annotation, evolutionary
algorithms, gene expression profiles, gene ontology,
knowledge discovery, multiobjective optimization",
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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