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1.  From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation 
Translational science, today, involves multidisciplinary teams of scientists rather than single scientists. Teams facilitate biologically meaningful and clinically consequential breakthroughs. There are a myriad of sources of data about investigators, physicians, research resources, clinical encounters, and expertise to promote team interaction; however, much of this information is not connected and is left siloed. Large amounts of data have been published as Linked Data (LD), but there still remains a significant gap in the representation and connection of research resources and clinical expertise data. The CTSAconnect project addresses the problem of fragmentation and incompatible coding of information by creating a Semantic Framework that facilitates the production and consumption of LD about biomedical research resources, clinical activities, as well as investigator and physician expertise.
PMCID: PMC3814477  PMID: 24303330
2.  An ontology-based method for secondary use of electronic dental record data  
A key question for healthcare is how to operationalize the vision of the Learning Healthcare System, in which electronic health record data become a continuous information source for quality assurance and research. This project presents an initial, ontology-based, method for secondary use of electronic dental record (EDR) data. We defined a set of dental clinical research questions; constructed the Oral Health and Disease Ontology (OHD); analyzed data from a commercial EDR database; and created a knowledge base, with the OHD used to represent clinical data about 4,500 patients from a single dental practice. Currently, the OHD includes 213 classes and reuses 1,658 classes from other ontologies. We have developed an initial set of SPARQL queries to allow extraction of data about patients, teeth, surfaces, restorations and findings. Further work will establish a complete, open and reproducible workflow for extracting and aggregating data from a variety of EDRs for research and quality assurance.
PMCID: PMC3845770  PMID: 24303273
3.  Research resources: curating the new eagle-i discovery system 
Development of biocuration processes and guidelines for new data types or projects is a challenging task. Each project finds its way toward defining annotation standards and ensuring data consistency with varying degrees of planning and different tools to support and/or report on consistency. Further, this process may be data type specific even within the context of a single project. This article describes our experiences with eagle-i, a 2-year pilot project to develop a federated network of data repositories in which unpublished, unshared or otherwise ‘invisible’ scientific resources could be inventoried and made accessible to the scientific community. During the course of eagle-i development, the main challenges we experienced related to the difficulty of collecting and curating data while the system and the data model were simultaneously built, and a deficiency and diversity of data management strategies in the laboratories from which the source data was obtained. We discuss our approach to biocuration and the importance of improving information management strategies to the research process, specifically with regard to the inventorying and usage of research resources. Finally, we highlight the commonalities and differences between eagle-i and similar efforts with the hope that our lessons learned will assist other biocuration endeavors.
Database URL: www.eagle-i.net
doi:10.1093/database/bar067
PMCID: PMC3308157  PMID: 22434835
4.  Uberon, an integrative multi-species anatomy ontology 
Genome Biology  2012;13(1):R5.
We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org
doi:10.1186/gb-2012-13-1-r5
PMCID: PMC3334586  PMID: 22293552

Results 1-4 (4)