BackgroundWe built the Drug Ontology (DrOn) because we required correct and consistent drug information in a format for use in semantic web applications, and no existing resource met this requirement or could be altered to meet it. One of the obstacles we faced when creating DrOn was the difficulty in reusing drug information from existing sources. The primary external source we have used at this stage in DrOn’s development is RxNorm, a standard drug terminology curated by the National Library of Medicine (NLM). To build DrOn, we (1) mined data from historical releases of RxNorm and (2) mapped many RxNorm entities to Chemical Entities of Biological Interest (ChEBI) classes, pulling relevant information from ChEBI while doing so.ResultsWe built DrOn in a modular fashion to facilitate simpler extension and development of the ontology and to allow reasoning and construction to scale. Classes derived from each source are serialized in separate modules. For example, the classes in DrOn that are programmatically derived from RxNorm are stored in a separate module and subsumed by classes in a manually-curated, realist, upper-level module of DrOn with terms such as 'clinical drug role’, 'tablet’, 'capsule’, etc.ConclusionsDrOn is a modular, extensible ontology of drug products, their ingredients, and their biological activity that avoids many of the fundamental flaws found in other, similar artifacts and meets the requirements of our comparative-effectiveness research use-case.

BackgroundThe Drug Ontology (DrOn) is an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms. We originally created DrOn for use cases in comparative effectiveness research, primarily to identify historically complete sets of United States National Drug Codes (NDCs) that represent packaged drug products, by the ingredient(s), mechanism(s) of action, and so on contained in those products. Although we had designed DrOn from the outset to carefully distinguish those entities that have a therapeutic indication from those entities that have a molecular mechanism of action, we had not previously represented in DrOn any particular therapeutic indication.ResultsIn this work, we add therapeutic indications for three research use cases: resistant hypertension, malaria, and opioid abuse research. We also added mechanisms of action for opioid analgesics and added 108 classes representing drug products in response to a large term request from the Program for Resistance, Immunology, Surveillance and Modeling of Malaria in Uganda (PRISM) project. The net result is a new version of DrOn, current to May 2016, that represents three major therapeutic classes of drugs and six new mechanisms of action.ConclusionsA therapeutic indication of a drug product is represented as a therapeutic function in DrOn. Adverse effects of drug products, as well as other therapeutic uses for which the drug product was not designed are dispositions. Our work provides a framework for representing additional therapeutic indications, adverse effects, and uses of drug products beyond their design. Our work also validated our past modeling decisions for specific types of mechanisms of action, namely effects mediated via receptor and/or enzyme binding. DrOn is available at: http://purl.obolibrary.org/obo/dron.owl. A smaller version without NDCs is available at: http://purl.obolibrary.org/obo/dron/dron-lite.owl

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesitycancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993.

BackgroundThe Ontology of Medically Related Social Entities (OMRSE) was initially developed in 2011 to provide a framework for modeling demographic data in Resource Description Framework/Web Ontology Language. It is built upon the Basic Formal Ontology and conforms to Open Biomedical Ontologies Foundry’s best practices.DescriptionWe report recent development of OMRSE which includes representations of organizations, roles, facilities, demographic data, enrollment in insurance plans, and data about socio-economic indicators.ConclusionsOMRSE’s coverage has been expanding in recent years to include a wide variety of classes and has been useful in several biomedical applications.

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization.