Semantic Data Integration for Integrative Cancer Biology Research
Small Business Information
INFOTECH SOFT, INC.
INFOTECH SOFT, INC., 9200 S DADELAND BLVD, STE 620, MIAMI, FL, 33156
AbstractDESCRIPTION (provided by applicant): Recent advances in high-throughput measurements of critical parameters related to cancer genesis and development have led to a wealth of cancer-related information available in public and private databases. To realize t he promise of dramatic advancement in integrative cancer research enabled by this rapidly expanding information, novel informatics tools that allow researchers to efficiently integrate this available data are needed. The main objective of this proposal is the development of a computerized system capable of integrating data and information from disparate sources in order to enable enhanced models of cancer processes. The proposed system aims to use and expand the capabilities of the Cancer Biomedical Informa tics Grid (caBIG) to allow users and applications to perform queries on cancer-related data sources at a conceptual level, utilizing the rich semantic information contained in caBIG in novel ways. The system contains mechanisms to expose these semantics us ing the Web Ontology Language (OWL), and to build and execute RDF-based querying using SPARQL, automatically identifying data services to query within the grid. An intuitive user interface providing multiple visualization and concept searching abilities is used to build queries and view results, while a programmatic interface is also provided for computer-to-computer interaction through Semantic Web standards. In Phase I of our proposal, the main algorithms and mechanisms of the proposed system, including t he exposure of ontology views and the conversion of queries from SPARQL into caBIG's common query language will be developed, and proof-of-concept prototypes will be tested to prove the feasibility of our design.The Cancer Biology Data Integration System i s an information integration solution that enables users to query cancer-related data using conceptual abstractions in a declarative manner more closely resembling the way in which research questions are stated. It models the rich semantic information cont ained in the Cancer Biomedical Informatics Grid (caBIG) as an ontology view, and uses Semantic Web standards to create and execute queries into caBIG-compatible data sources.
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