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Integration of Data

This service area includes integrating experimental measurements with appropriate clinical metadata for research projects that utilize web-based technologies. All software, when practical and applicable, will be based on open-source software and made freely available to all cancer centers and academic institutions to promote data sharing. A key to developing useful computational tools to support biomedical research is the creation of data models that properly represent molecular and clinical data (particularly incorporating social, environmental, and longitudinal data) to support, for example, clinical trials, molecular diagnostics, and patient management. Each investigator-initiated project requires flexible, scalable, and portable data resources that are based on standardized data models, vocabularies, and metadata schemas. The Core assists researchers by providing flexible data models that deliver the necessary architecture, so that when new data types emerge in the quick-paced fields of genomics and proteomics, these data can be integrated into the data resource and linked to previous data types. Additionally, data resources are designed to provide scalability to accommodate the enormous amounts of data generated by high-throughput technologies and incorporate portability by using standards such as XML for data exchange. Developed resources allow the investigator to link genotype to phenotype using structured vocabularies (GO [Gene Ontology] and DO [Disease Ontology]), and seamlessly navigate through public annotation resources, such as GenBank, EnsEMBL, SwissProt, PDB, KEGG, and BIND, for evidence of the biochemical mechanisms of disease.