OBJECTIVE: Visual representations generally serve as supplements to information, rather than as bearers of computable information themselves. Our objective is to develop a method for creating semantically-augmented graphic libraries that will serve as visual standards and can be implemented as visual assets in intelligent information systems. METHODS: Graphics were developed using a composable approach and specified using SVG. OWL was used to represent the entities of our system, which include elements, units, graphics, graphic libraries, and library collections. A graph database serves as our data management system. Semantics are applied at multiple levels: (a) each element is associated with a semantic style class to link visual style to semantic meaning, (b) graphics are described using object properties and data properties, (c) relationships are specified between graphics, and (d) mappings are made between the graphics and outside resources. RESULTS: The Graphic Library web application enables users to browse the libraries, view information pages for each graphic, and download individual graphics. We demonstrate how SPARQL can be employed to query the graphics database and the APIs can be used to retrieve the graphics and associated data for applications. In addition, this work shows that our method of designing composable graphics is well-suited to depicting variations in human anatomy. CONCLUSION: This work provides a bridge between visual communication and the field of knowledge representation. We demonstrate a method for creating visual standards that are compatible with practices in biomedical ontology and implement a system for making them accessible to information systems.

BACKGROUND: A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools. METHODS: We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. RESULTS: A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. DISCUSSION AND CONCLUSION: As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates.

There is a significant need for data integration capabilities in the scientific domain, which has manifested itself as products in the commercial world as well as academia. However, in our experiences in dealing with biological data it has become apparent to us that existing data integration products do not handle uncertainties in the data very well. This leads to systems that often produce an explosion of less relevant answers which subsequently leads to a loss of more relevant answers by overloading the user. How to incorporate functionality into data integration systems to properly handle uncertainties and make results more useful has become an important research question. In this paper we describe an enhanced general-purpose data integration system which incorporates uncertainty metrics within a formal probabilistic framework. Additionally, for evaluation purposes, we have implemented a use case scenario which utilizes biological data sources and performed a study which provides validation of system query results.

The Digital Anatomist Foundational Model of Anatomy (FMA) is a large semantic network of more than 100,000 terms that refer to the anatomical entities, which together with 1.6 million structural relationships symbolically represent the physical organization of the human body. Evaluation of such a large knowledge base by domain experts is challenging because of the sheer size of the resource and the need to evaluate not just classes but also relationships. To meet this challenge, the authors have developed a relation-centric query interface, called Emily, that is able to query the entire range of classes and relationships in the FMA, yet is simple to use by a domain expert. Formative evaluation of this interface considered the ability of Emily to formulate queries based on standard anatomy examination questions, as well as the processing speed of the query engine. Results show that Emily is able to express 90% of the examination questions submitted to it and that processing time is generally 1 second or less, but can be much longer for complex queries. These results suggest that Emily will be a very useful tool, not only for evaluating the FMA, but also for querying and evaluating other large semantic networks.