Featured Article: Genotation: Actionable knowledge for the scientific reader.

We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org The Genomine algorithm identifies gene symbols with an accuracy shown by .65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug-gene relationships, 5981 gene-disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge.

Clinical data integration of distributed data sources using Health Level Seven (HL7) v3-RIM mapping.

BACKGROUND: Health information exchange and health information integration has become one of the top priorities for healthcare systems across institutions and hospitals. Most organizations and establishments implement health information exchange and integration in order to support meaningful information retrieval among their disparate healthcare systems. The challenges that prevent efficient health information integration for heterogeneous data sources are the lack of a common standard to support mapping across distributed data sources and the numerous and diverse healthcare domains. Health Level Seven (HL7) is a standards development organization which creates standards, but is itself not the standard. They create the Reference Information Model. RIM is developed by HL7's technical committees. It is a standardized abstract representation of HL7 data across all the domains of health care. In this article, we aim to present a design and a prototype implementation of HL7 v3-RIM mapping for information integration of distributed clinical data sources. The implementation enables the user to retrieve and search information that has been integrated using HL7 v3-RIM technology from disparate health care systems. METHOD AND RESULTS: We designed and developed a prototype implementation of HL7 v3-RIM mapping function to integrate distributed clinical data sources using R-MIM classes from HL7 v3-RIM as a global view along with a collaborative centralized web-based mapping tool to tackle the evolution of both global and local schemas. Our prototype was implemented and integrated with a Clinical Database management Systems CDMS as a plug-in module. We tested the prototype system with some use case scenarios for distributed clinical data sources across several legacy CDMS. The results have been effective in improving information delivery, completing tasks that would have been otherwise difficult to accomplish, and reducing the time required to finish tasks which are used in collaborative information retrieval and sharing with other systems. CONCLUSIONS: We created a prototype implementation of HL7 v3-RIM mapping for information integration between distributed clinical data sources to promote collaborative healthcare and translational research. The prototype has effectively and efficiently ensured the accuracy of the information and knowledge extractions for systems that have been integrated.