e-MIR2: a public online inventory of medical informatics resources

Background. Over the last years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for Medical Informatics (MI) field, so that locating and accessing them currently remains a hard and time-consuming task. Description. We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources? names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different taxonomies by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the taxonomies. The classification algorithm identifies the categories associated to resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions. We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contained 282 resources at the time of writing. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers

Controlled vocabularies and semantics in systems biology

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments

Leadership in complex networks: the importance of network position and strategic action in a translational cancer research network

BackgroundLeadership behaviour in complex networks is under-researched, and little has been written concerning leadership of translational research networks (TRNs) that take discoveries made ‘at the bench’ and translate them into practices used ‘at the bedside.’ Understanding leaders’ opportunities and behaviours within TRNs working to solve this key problem in implementing evidence into clinical practice is therefore important. This study explored the network position of governing body members and perceptions of their role in a new TRN in Sydney, Australia. The paper asks three questions: Firstly, do the formal, mandated leaders of this TRN hold key positions of centrality or brokerage in the informal social network of collaborative ties? Secondly, if so, do they recognise the leadership opportunities that their network positions afford them? Thirdly, what activities associated with these key roles do they believe will maximise the TRN’s success? MethodsSemi-structured interviews of all 14 governing body members conducted in early 2012 explored perceptions of their roles and sought comments on a list of activities drawn from review of successful transdisciplinary collaboratives combined with central and brokerage roles. An on-line, whole network survey of all 68 TRN members sought to understand and map existing collaborative connections. Leaders’ positions in the network were assessed using UCInet, and graphs were generated in NetDraw. ResultsSocial network analysis identified that governing body members had high centrality and high brokerage potential in the informal network of work-related ties. Interviews showed perceived challenges including ‘silos’ and the mismatch between academic and clinical goals of research. Governing body members recognised their central positions, which would facilitate the leadership roles of leading, making decisions, and providing expert advice necessary for the co-ordination of effort and relevant input across domains. Brokerage potential was recognised in their clearly understood role of representing a specialty, campus or research group on the governing body to provide strategic linkages. Facilitation, mentoring and resolving conflicts within more localised project teams were spoken of as something ‘we do all the time anyway,’ as well as something they would do if called upon. These leadership roles are all linked with successful collaborative endeavours in other fields. ConclusionsThis paper links the empirical findings of the social network analysis with the qualitative findings of the interviews to show that the leaders’ perceptions of their roles accord with both the potential inherent in their network positions as well as actual activities known to increase the success of transdisciplinary teams. Understanding this is key to successful TRNs

Network structure and the role of key players in a translational cancer research network: a study protocol

Introduction: Translational research networks are a deliberate strategy to bridge the gulf between biomedical research and clinical practice through interdisciplinary collaboration, supportive funding and infrastructure. The social network approach examines how the structure of the network and players who hold important positions within it constrain or enable function. This information can be used to guide network management and optimise its operations. The aim of this study was to describe the structure of a translational cancer research network (TCRN) in Australia over its first year, identify the key players within the network and explore these players'opportunities and constraints in maximising important network collaborations. Methods and analysis: This study deploys a mixed-method longitudinal design using social network analysis augmented by interviews and review of TCRN documents. The study will use network documents and interviews with governing body members to explore the broader context into which the network is embedded as well as the perceptions and expectations of members. Of particular interest are the attitudes and perceptions of clinicians compared with those of researchers. A co-authorship network will be constructed of TCRN members using journal and citation databases to assess the success of past pre-network collaborations. Two whole network social network surveys will be administered 12 months apart and parameters such as density, clustering, centrality and betweenness centrality computed and compared using UCINET and Netdraw. Key players will be identified and interviewed to understand the specific activities, barriers and enablers they face in that role. Ethics and dissemination: Ethics approvals were obtained from the University of New South Wales, South Eastern Sydney Northern Sector Local Health Network and Calvary Health Care Sydney. Results will be discussed with members of the TCRN, submitted to relevant journals and presented as oral presentations to clinicians, researchers and policymakers.8 page(s

Who are the key players in a new translational research network?

BackgroundProfessional networks are used increasingly in health care to bring together members from different sites and professions to work collaboratively. Key players within these networks are known to affect network function through their central or brokerage position and are therefore of interest to those who seek to optimise network efficiency. However, their identity may not be apparent. This study using social network analysis to ask: (1) Who are the key players of a new translational research network (TRN)? (2) Do they have characteristics in common? (3) Are they recognisable as powerful, influential or well connected individuals? MethodsTRN members were asked to complete an on-line, whole network survey which collected demographic information expected to be associated with key player roles, and social network questions about collaboration in current TRN projects. Three questions asked who they perceived as powerful, influential and well connected. Indegree and betweenness centrality values were used to determine key player status in the actual and perceived networks and tested for association with demographic and descriptive variables using chi square analyses. ResultsResponse rate for the online survey was 76.4% (52/68). The TRN director and manager were identified as key players along with six other members. Only two of nine variables were associated with actual key player status; none with perceived. The main finding was the mismatch between actual and perceived brokers. Members correctly identified two of the three central actors (the two mandated key roles director and manager) but there were only three correctly identified actual brokers among the 19 perceived brokers. Possible reasons for the mismatch include overlapping structures and weak knowledge of members. ConclusionsThe importance of correctly identifying these key players is discussed in terms of network interventions to improve efficiency

Patterns of collaboration in complex networks: The example of a translational research network

BackgroundThis paper examines collaboration in a complex translational cancer research network (TRN) made up of a range of hospital-based clinicians and university-based researchers. We examine the phenomenon of close-knit and often introspective clusters of people (silos) and test the extent that factors associated with this clustering (geography, profession and past experience) influence patterns of current and future collaboration on TRN projects. Understanding more of these patterns, especially the gaps or barriers between members, will help network leaders to manage subgroups and promote connectivity crucial to efficient network function.MethodsAn on-line, whole network survey was used to collect attribute and relationship data from all members of the new TRN based in New South Wales, Australia in early 2012. The 68 members were drawn from six separate hospital and university campuses. Social network analysis with UCInet tested the effects of geographic proximity, profession, past research experience, strength of ties and previous collaborations on past, present and future intended partnering.ResultsGeographic proximity and past working relationships both had significant effects on the choice of current collaboration partners. Future intended collaborations included a significant number of weak ties and ties based on other members’ reputations implying that the TRN has provided new opportunities for partnership. Professional grouping, a significant barrier discussed in the translational research literature, influenced past collaborations but not current or future collaborations, possibly through the mediation of network brokers.ConclusionsSince geographic proximity is important in the choice of collaborators a dispersed network such as this could consider enhancing cross site interactions by improving virtual communication technology and use, increasing social interactions apart from project related work, and maximising opportunities to meet members from other sites. Key network players have an important brokerage role facilitating linkages between groups

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

A hybrid human and machine resource curation pipeline for the Neuroscience Information Framework

The breadth of information resources available to researchers on the Internet continues to expand, particularly in light of recently implemented data-sharing policies required by funding agencies. However, the nature of dense, multifaceted neuroscience data and the design of contemporary search engine systems makes efficient, reliable and relevant discovery of such information a significant challenge. This challenge is specifically pertinent for online databases, whose dynamic content is ‘hidden’ from search engines. The Neuroscience Information Framework (NIF; http://www.neuinfo.org) was funded by the NIH Blueprint for Neuroscience Research to address the problem of finding and utilizing neuroscience-relevant resources such as software tools, data sets, experimental animals and antibodies across the Internet. From the outset, NIF sought to provide an accounting of available resources, whereas developing technical solutions to finding, accessing and utilizing them. The curators therefore, are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping the contents current. In the initial phases of the project, all aspects of the registration and curation processes were manual. However, as the number of resources grew, manual curation became impractical. This report describes our experiences and successes with developing automated resource discovery and semiautomated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. We also describe the DISCO framework, a suite of automated web services that significantly reduce manual curation efforts to periodically check for resource updates. Lastly, we discuss DOMEO, a semi-automated annotation tool that improves the discovery and curation of resources that are not necessarily website-based (i.e. reagents, software tools). Although the ultimate goal of automation was to reduce the workload of the curators, it has resulted in valuable analytic by-products that address accessibility, use and citation of resources that can now be shared with resource owners and the larger scientific community

NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic