A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database
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 con ceptual clustering approaches normally recover the most obvious
relations, but fail to discover the lessfrequent 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), re lying on the NSGA-II multiobjective (MO) genetic algorithm. We
apply this methodology to identify conceptual models in struc tural 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.Ministerio de Ciencia y Tecnología TIC-2003-00877Ministerio de Ciencia y Tecnología BIO2004-0270EMinisterio de Ciencia y Tecnología TIN2006-1287
