title: Ontology-Based Analysis of Microarray Data.
abstract: The importance of semantic-based methods and algorithms for the analysis and management of biological data is growing for two main reasons. From a biological side, knowledge contained in ontologies is more and more accurate and complete, from a computational side, recent algorithms are using in a valuable way such knowledge. Here we focus on semantic-based management and analysis of protein interaction networks referring to all the approaches of analysis of protein-protein interaction data that uses knowledge encoded into biological ontologies. Semantic approaches for studying high-throughput data have been largely used in the past to mine genomic and expression data. Recently, the emergence of network approaches for investigating molecular machineries has stimulated in a parallel way the introduction of semantic-based techniques for analysis and management of network data. The application of these computational approaches to the study of microarray data can broad the application scenario of them and simultaneously can help the understanding of disease development and progress.
authors.affiliation: Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa-Localita Germaneto, Catanzaro, 88100, Italy. agapito@unicz.it.
authors.forename: Agapito
authors.initials: A
authors.lastname: Giuseppe
authors.affiliation: Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa-Localita Germaneto, Catanzaro, 88100, Italy. m.milano@unicz.it.
authors.forename: Marianna
authors.initials: M
authors.lastname: Milano
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods; D012660:Semantics
keywords: Bi-clustering; Data mining; Expression patterns; Microarray