semCDI: A Query Formulation for Semantic Data Integration in caBIG

Shironoshita, E. Patrick; Jean-Mary, Yves R.; Bradley, Ray M.; Kabuka, Mansur R.

doi:10.1197/jamia.m2732

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…The researcher who adopts the current LEAD ™ UML model can add more attributes or modify the existing attributes in the classes. Readers who are interested in query formulation techniques on semantic data can further reference papers by Shironoshita et al and Baer et al 35,36…”

Section: Resultsmentioning

confidence: 99%

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System

et al. 2009

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Lymphomas are the fi fth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute's Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data.

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Section: Resultsmentioning

confidence: 99%

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System

et al. 2009

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“…semCDI query formulation uses a view of caBIG semantic concepts, metadata, and data as an ontology [30]. The result was that OWL annotation properties are used to represent metadata on OWL constructs and are not considered for reasoning purposes.…”

Section: Discussionmentioning

confidence: 99%

Developing a semantically rich ontology for the biobank-administration domain

et al. 2013

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BackgroundBiobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contact's affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases.ResultsWe created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S.ConclusionsIn conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology.

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“…The two approaches could therefore greatly benefit from each other; indeed, a call for Semantic Web opportunities has been launched by the caBIG community [41]. The semCDI [42] and Corvus [43] projects, for example, have already developed extensive work towards modeling and integrating the various data models available at caBIG including the availability of SPARQL engines. The architecture described in this report could thus be easily integrated with caBIG datasets that are made available as RDF or if a SPARQL endpoint is provided.…”

Section: Discussionmentioning

confidence: 99%

Exposing the cancer genome atlas as a SPARQL endpoint

Deus

Veiga

Freire

et al. 2010

Journal of Biomedical Informatics

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The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source.

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semCDI: A Query Formulation for Semantic Data Integration in caBIG

Abstract: semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.

Cited by 17 publications

References 13 publications

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System

Developing a semantically rich ontology for the biobank-administration domain

Exposing the cancer genome atlas as a SPARQL endpoint

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semCDI: A Query Formulation for Semantic Data Integration in caBIG

Abstract: semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.

Cited by 17 publications

References 13 publications

Development of the Lymphoma Enterprise Architecture Database: A caBIG™ Silver Level Compliant System

Development of the Lymphoma Enterprise Architecture Database: A caBIG™ Silver Level Compliant System

Developing a semantically rich ontology for the biobank-administration domain

Exposing the cancer genome atlas as a SPARQL endpoint

Contact Info

Product

Resources

About

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System

Development of the Lymphoma Enterprise Architecture Database: A caBIG^™ Silver Level Compliant System