Towards Reducing Health Information Inequities in the Caribbean: The Eastern Caribbean Health Outcomes Research Network Data Sharing Platform Usability Study.

There is a dearth of health research among Caribbean populations. Underrepresented individuals are affected by structural and data inequities that limit the usefulness, availability, and accessibility to health information systems and research-generated data. To overcome this limitation, a data sharing platform was created for the Eastern Caribbean Health Outcomes Research Network Cohort Study. This study aimed to evaluate the usability of the platform. Usability testing was conducted remotely, via video conferencing, using a cognitive walkthrough and think-aloud protocol. Participants completed a self-administered web-based survey which included an adapted version of the System Usability Scale (SUS). The results showed (N=16) overall average SUS score was 73.1 (SD±21.0), translating to a 'good' usability rating. Most recommendations for improvement focused on navigation and error prevention. Participatory data sharing platforms have the potential to reduce health information inequities in the Caribbean, however, usability testing should be conducted to improve user satisfaction and increase engagement.

Toward Reducing Health Information Inequities in the Caribbean: Our Experience Building a Participatory Health Informatics Project.

Precision medicine seeks to leverage technology to improve the health for all individuals. Successful health information systems rely fundamentally on the integration and sharing of data from a range of disparate sources. In many settings, basic infrastructure inequities exist that limit the usefulness of health information systems. We discuss the work of the Yale Transdisciplinary Collaborative Center for Health Disparities focused on Precision Medicine, which aims to improve the health of people in the Caribbean and Caribbean diaspora by leveraging precision medicine approaches. We describe a participatory informatics approach to sharing data as a potential mechanism to reducing inequities in the existing data infrastructure.

Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons.

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

The Share Project: Building Capacity of Justice-Involved Individuals, Policymakers, and Researchers to Collectively Transform Health Care Delivery.

The Share Project (TSP), a US health justice initiative, convened key stakeholders to advance the use of inclusive research methods and data sharing to engage groups that are typically marginalized from research. TSP trained justice-involved patients, community health workers, policymakers, and researchers in participatory research and the use of a data-sharing platform developed with justice-involved patients. The platform allowed users to analyze health and criminal justice data to develop new research that is patient driven and responsive to the needs of providers.

A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as "actionable"- requiring close follow up, or "non-actionable"- requiring standard or no follow up. We then used 600 distinct pathology reports from 6 different VA sites as our test corpus. Analysis of our test corpus shows that our NLP approach yields 98.5% accuracy in identifying cases that required close clinical follow up. By integrating this into our cancer care tracking system, our goal is to ensure that patients with worrisome pathology receive appropriate and timely follow-up and care.

Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

Marginalized communities are rarely included in the planning of research relevant to their own health or have access to research data. We implemented a community-based participatory research approach in developing a new health informatics system, WARP (Web Analytics Research Platform), to enable stakeholders to access and analyze research data. We leveraged data from a cohort study of 751 patients in the Transitions Clinic Network (TCN), a network of clinical programs serving patients with a history of incarceration. WARP holds de-identified, patient data, streamlines data processing (i.e. transformation, archival, and partitioning), and has a web analytic tool for users to perform statistical analyses. We used feedback from focus groups of patients with a history of incarceration and workshops with TCN research teams, including patients, community health workers and policymakers, to develop WARP. Our approach advances mechanisms to engage stakeholders in research. Future work will evaluate its effect on community-engagement in research.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

ORDB, HORDE, ODORactor and other on-line knowledge resources of olfactory receptor-odorant interactions.

We present here an exploration of the evolution of three well-established, web-based resources dedicated to the dissemination of information related to olfactory receptors (ORs) and their functional ligands, odorants. These resources are: the Olfactory Receptor Database (ORDB), the Human Olfactory Data Explorer (HORDE) and ODORactor. ORDB is a repository of genomic and proteomic information related to ORs and other chemosensory receptors, such as taste and pheromone receptors. Three companion databases closely integrated with ORDB are OdorDB, ORModelDB and OdorMapDB; these resources are part of the SenseLab suite of databases (http://senselab.med.yale.edu). HORDE (http://genome.weizmann.ac.il/horde/) is a semi-automatically populated database of the OR repertoires of human and several mammals. ODORactor (http://mdl.shsmu.edu.cn/ODORactor/) provides information related to OR-odorant interactions from the perspective of the odorant. All three resources are connected to each other via web-links.Database URL: http://senselab.med.yale.edu; http://genome.weizmann.ac.il/horde/; http://mdl.shsmu.edu.cn/ODORactor/.

Extending the NIF DISCO framework to automate complex workflow: coordinating the harvest and integration of data from diverse neuroscience information resources.

This paper describes how DISCO, the data aggregator that supports the Neuroscience Information Framework (NIF), has been extended to play a central role in automating the complex workflow required to support and coordinate the NIF's data integration capabilities. The NIF is an NIH Neuroscience Blueprint initiative designed to help researchers access the wealth of data related to the neurosciences available via the Internet. A central component is the NIF Federation, a searchable database that currently contains data from 231 data and information resources regularly harvested, updated, and warehoused in the DISCO system. In the past several years, DISCO has greatly extended its functionality and has evolved to play a central role in automating the complex, ongoing process of harvesting, validating, integrating, and displaying neuroscience data from a growing set of participating resources. This paper provides an overview of DISCO's current capabilities and discusses a number of the challenges and future directions related to the process of coordinating the integration of neuroscience data within the NIF Federation.

Databases in SenseLab for the genomics, proteomics, and function of olfactory receptors.

We present here, the salient aspects of three databases: Olfactory Receptor Database (ORDB) is a repository of genomics and proteomics information of ORs; OdorDB stores information related to odorous compounds, specifically identifying those that have been shown to interact with olfactory rectors; and OdorModelDB disseminates information related to computational models of olfactory receptors (ORs). The data stored among these databases is integrated. Presented in this chapter are descriptions of these resources, which are part of the SenseLab suite of databases, a discussion of the computational infrastructure that enhances the efficacy of information storage, retrieval, dissemination, and automated data population from external sources.

The NIF DISCO Framework: facilitating automated integration of neuroscience content on the web.

This paper describes the capabilities of DISCO, an extensible approach that supports integrative Web-based information dissemination. DISCO is a component of the Neuroscience Information Framework (NIF), an NIH Neuroscience Blueprint initiative that facilitates integrated access to diverse neuroscience resources via the Internet. DISCO facilitates the automated maintenance of several distinct capabilities using a collection of files 1) that are maintained locally by the developers of participating neuroscience resources and 2) that are "harvested" on a regular basis by a central DISCO server. This approach allows central NIF capabilities to be updated as each resource's content changes over time. DISCO currently supports the following capabilities: 1) resource descriptions, 2) "LinkOut" to a resource's data items from NCBI Entrez resources such as PubMed, 3) Web-based interoperation with a resource, 4) sharing a resource's lexicon and ontology, 5) sharing a resource's database schema, and 6) participation by the resource in neuroscience-related RSS news dissemination. The developers of a resource are free to choose which DISCO capabilities their resource will participate in. Although DISCO is used by NIF to facilitate neuroscience data integration, its capabilities have general applicability to other areas of research.

Implementing description-logic rules for SNOMED-CT attributes through a table-driven approach.

Maintaining a large controlled biomedical vocabulary requires ensuring the content's internal consistency. This is done through rules, specified by the vocabulary's curators, which denote how the vocabulary's concepts should be defined. When individual organizations deploy such vocabularies, local concepts are typically added and linked to concepts in the main vocabulary: the process of maintaining and linking local content should follow the same rules. The operation of content-maintenance software can be facilitated by maintaining such rules in computable form. In this paper, we demonstrate how to implement computable rules for attribute usage in SNOMED CT using a table-driven approach where a given rule is expressed as one or more rows in a table and is consulted by generic code. This approach, which is tailored to database implementations, is computationally efficient and allows new attribute-definition rules to be created as data while needing minimal or no code modification.

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.

OBJECTIVE: Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. METHODS: Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. CONCLUSION: We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/.

Entrez Neuron RDFa: a pragmatic semantic web application for data integration in neuroscience research.

The amount of biomedical data available in Semantic Web formats has been rapidly growing in recent years. While these formats are machine-friendly, user-friendly web interfaces allowing easy querying of these data are typically lacking. We present "Entrez Neuron", a pilot neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies. These ontologies describe neuronal structures, physiology, mathematical models and microscopy images. The returned query results are organized hierarchically according to brain architecture. Where possible, the application makes use of entities from the Open Biomedical Ontologies (OBO) and the 'HCLS knowledgebase' developed by the W3C Interest Group for Health Care and Life Science. It makes use of the emerging RDFa standard to embed ontology fragments and semantic annotations within its HTML-based user interface. The application and underlying ontologies demonstrate how Semantic Web technologies can be used for information integration within a curated information repository and between curated information repositories. It also demonstrates how information integration can be accomplished on the client side, through simple copying and pasting of portions of documents that contain RDFa markup.

Automated database mediation using ontological metadata mappings.

OBJECTIVE: To devise an automated approach for integrating federated database information using database ontologies constructed from their extended metadata. BACKGROUND: One challenge of database federation is that the granularity of representation of equivalent data varies across systems. Dealing effectively with this problem is analogous to dealing with precoordinated vs. postcoordinated concepts in biomedical ontologies. MODEL DESCRIPTION: The authors describe an approach based on ontological metadata mapping rules defined with elements of a global vocabulary, which allows a query specified at one granularity level to fetch data, where possible, from databases within the federation that use different granularities. This is implemented in OntoMediator, a newly developed production component of our previously described Query Integrator System. OntoMediator's operation is illustrated with a query that accesses three geographically separate, interoperating databases. An example based on SNOMED also illustrates the applicability of high-level rules to support the enforcement of constraints that can prevent inappropriate curator or power-user actions. SUMMARY: A rule-based framework simplifies the design and maintenance of systems where categories of data must be mapped to each other, for the purpose of either cross-database query or for curation of the contents of compositional controlled vocabularies.

Approaches to neuroscience data integration.

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

The NIF LinkOut broker: a web resource to facilitate federated data integration using NCBI identifiers.

This paper describes the NIF LinkOut Broker (NLB) that has been built as part of the Neuroscience Information Framework (NIF) project. The NLB is designed to coordinate the assembly of links to neuroscience information items (e.g., experimental data, knowledge bases, and software tools) that are (1) accessible via the Web, and (2) related to entries in the National Center for Biotechnology Information's (NCBI's) Entrez system. The NLB collects these links from each resource and passes them to the NCBI which incorporates them into its Entrez LinkOut service. In this way, an Entrez user looking at a specific Entrez entry can LinkOut directly to related neuroscience information. The information stored in the NLB can also be utilized in other ways. A second approach, which is operational on a pilot basis, is for the NLB Web server to create dynamically its own Web page of LinkOut links for each NCBI identifier in the NLB database. This approach can allow other resources (in addition to the NCBI Entrez) to LinkOut to related neuroscience information. The paper describes the current NLB system and discusses certain design issues that arose during its implementation.

The neuroscience information framework: a data and knowledge environment for neuroscience.

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.

Issues in the design of a pilot concept-based query interface for the neuroinformatics information framework.

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF).

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.