Hierarchical Biological Pathway Data Integration and Mining

information embedded within them are addressed. Today, a bioinformatics information system typically deals with large data sets reaching a total volume of about one terabyte [25]. Such a system serves many purposes;  User can select the data sources and assign confidence to each selected data source  It organizes existing data to facilitate complex queries  It infers relationships based on the stored data and subsequently predicts missing attribute values and incoming information based on multidimensional data.  Data marts (extension of data warehouse) support different query requests. 2. Data management and integration The Pathway Resource List contains over 150 biological pathway databases and is growing [26]. Usually, first step for the user is to identify a subset of these data sources for integration. To consolidate all the knowledge for a particular organism, extract the pathways from each database need to be extracted and transformed into a standard data representation before integration. Representation of the pathway data in each data source poses another challenge as each pathway modality has its own specific representation issues which must be understood before attempting integration across modalities. For example, metabolic pathways, signal transduction pathways, protein-protein interaction, gene regulation etc. Commonly employed styles of data integration may be implemented in different contexts and under requirements, in order to reuse the data across applications for research collaboration. Some of the data integration and management efforts are presented in [27-32]. Several major approaches have been proposed for data integration, which can be roughly classified into five groups [33-34] namely; data warehousing, federated databasing, serviceoriented integration, semantic integration and wiki-based integration. Across all of these groups, to a significant extent, an increasingly important component of data integration is the community effort in developing a variety of biomedical ontologies to deal in a more specific manner with the technicality and globality of descriptors and identifiers of information that has to be shared and integrated across various resources. Variety of approaches for data integration is discussed below.