The Cancer Informatics Shared Resource exists to provide infrastructure and expertise that enable Cancer Center members and activities to adopt and deploy good information practices. These low-profile but crucial resources serve to integrate and responsibly curate Cancer Center information throughout the enterprise. Data integrity, consistency, comparability, and interoperability are thereby enhanced. Data-dependent activities and services throughout the Cancer Center are rendered more efficient and reliable as a consequence. The Resource is divided into four teams and an administrative unit. The four teams are: 1) The Cancer Database and Registry (CDR) Team which provides data design, data harvesting, and data integration of all pertinent cancer information for all cancer patients within the Mayo enterprise. The CDR resource serves as the heart of Cancer Center informatics operations, services, data design, and integration. It serves by design as the core for study and disease-specific database extensions. 2) The Study and Disease Databases Team was created to exploit our investment in the CDR and fashion database extensions atop the core database, without duplicating core content. This team also engages in the design and deployment of data access methods relevant to cancer investigators that are latent in the Mayo-IBM Life Sciences Warehouse project, a major partnership between Mayo and IBM which intends to systematically harvest, organize, and responsibly deliver patient data - from genotype to phenotype. 3) The Bioinformatics Team has been assembled from multiple resources to assist Mayo cancer investigators in the use of bioinformatics tools to pinpoint the genetic roots of cancer. Services include the installation and maintenance of genomic analysis software and their associated databases on shared hardware platforms that locally support the computationally intensive requirements of the Cancer Center. And 4) the Informatics Infrastructure Team comprises a multidisciplinary cadre of investigators who contribute importantly to achieving clinical and genomic data that are comparable, interoperable, and consistent. Examples of such infrastructure services include the integration and support of standard terminologies and data structures, the invocation of Metadata for query and data navigation, and the normalization of clinical data harvested from multiple clinical domains and physical locations into comparable and consistent data elements.
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