National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease

Assessing an ontology for the representation of clinical protocols in decision support systems

In order to assess the expressiveness of the CompGuide ontology for Clinical Practice Guidelines, a study was conducted with fourteen students of the Integrated Masters in Biomedical Engineering from the University of Minho in Portugal to whom it was proposed the representation of multiple guidelines according to the ontology. They were then asked to evaluate the ontology through a questionnaire and written reports. Although the results seem promising, there is the need for significant improvements mainly in: the representation of medication prescriptions, the tasks used to retrieve information from the patient, the diversity of actions offered by the ontology, the expressiveness of conditions regarding the state of a patient, and temporal constraints.FCT - Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio

Is the crowd better as an assistant or a replacement in ontology engineering? An exploration through the lens of the Gene Ontology

Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential overcome the limited accuracy and scalability found in current ontology quality assurance approaches. Crowd-based methods have identified errors in SNOMED CT, a large, clinical ontology, with an accuracy similar to that of experts, suggesting that crowdsourcing is indeed a feasible approach for identifying ontology errors. This work uses that same crowd-based methodology, as well as a panel of experts, to verify a subset of the Gene Ontology (200 relationships). Experts identified 16 errors, generally in relationships referencing acids and metals. The crowd performed poorly in identifying those errors, with an area under the receiver operating characteristic curve ranging from 0.44 to 0.73, depending on the methods configuration. However, when the crowd verified what experts considered to be easy relationships with useful definitions, they performed reasonably well. Notably, there are significantly fewer Google search results for Gene Ontology concepts than SNOMED CT concepts. This disparity may account for the difference in performance – fewer search results indicate a more difficult task for the worker. The number of Internet search results could serve as a method to assess which tasks are appropriate for the crowd. These results suggest that the crowd fits better as an expert assistant, helping experts with their verification by completing the easy tasks and allowing experts to focus on the difficult tasks, rather than an expert replacement

BioPortal: ontologies and integrated data resources at the click of a mouse

Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources

Making Neural Networks FAIR

Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles. Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable neural networks to data scientists

Impact of Diabetes and Its Treatment on Cognitive Function Among Adolescents Who Participated in the Diabetes Control and Complications Trial

OBJECTIVE—The purpose of this study was to evaluate whether severe hypoglycemia or intensive therapy affects cognitive performance over time in a subgroup of patients who were aged 13–19 years at entry in the Diabetes Control and Complications Trial (DCCT)

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts

Cerebral Perfusion and Aortic Stiffness Are Independent Predictors of White Matter Brain Atrophy in Type 1 Diabetic Patients Assessed With Magnetic Resonance Imaging

OBJECTIVE-To identify vascular mechanisms of brain atrophy in type 1 diabetes mellitus (DM) patients by investigating the relationship between brain volumes and cerebral perfusion and aortic stiffness using magnetic resonance imaging (MRI). RESEARCH DESIGN AND METHODS-Approval from the local institutional review board was obtained, and patients gave informed consent. Fifty-one type 1 DM patients (30 men; mean age 44 +/- 11 years; mean DM duration 23 +/- 12 years) and 34 age- and sex-matched healthy control subjects were prospectively enrolled. Exclusion criteria comprised hypertension, stroke, aortic disease, and standard MRI contraindications. White matter (WM) and gray matter (GM) brain volumes, total cerebral blood flow (tCBF), total brain perfusion, and aortic pulse wave velocity (PWV) were assessed using MRI. Multivariable linear regression analysis was used for statistics, with covariates age, sex, mean arterial pressure, BMI, smoking, heart rate, DM duration, and HbA(1c). RESULTS-Both WM and GM brain volumes were decreased in type 1 DM patients compared with control subjects (WM P = 0.04; respective GM P = 0.03). Total brain perfusion was increased in type 1 DM compared with control subjects (beta = -0.219, P < 0.05). Total CBF and aortic PWV predicted WM brain volume (beta = 0.352, P = 0.024 for tCBF; respective beta = 0.458, P = 0.016 for aortic PWV) in type 1 DM. Age was the independent predictor of GM brain volume (beta = -0.695, P < 0.001). CONCLUSIONS-Type 1 DM patients without hypertension showed WM and GM volume loss compared with control subjects concomitant with a relative increased brain perfusion. Total CBF and stiffness of the aorta independently predicted WM brain atrophy in type 1 DM. Only age predicted GM brain atrophy.Cardiovascular Aspects of Radiolog

A system for the management of clinical tasks throughout the clinical process with notification features

Computer-Interpretable Guidelines have been associated with a higher integration of standard practices in the daily context of health care institutions. The Clinical Decision Support Systems that deliver these machine-interpretable recommendations usually follow a Q & A style of communication, retrieving information from the user or a clinical repository and performing reasoning upon it, based on the rules from Clinical Practice Guidelines. However, these systems are limited in the reach they are capable of achieving as they were initially conceived for use in very specific moments of the clinical process, namely in physician appointments. The purpose of this work is thus to present a system that, in addition to Q & A reasoning, is equipped with other functionalities such as the scheduling and temporal management of clinical tasks, the mapping of these tasks onto an agenda of activities to allow an easy consultation by health care professionals, and notifications that let health care professionals know of task enactment times and information collection times. In this way, the system ensures the delivery of procedures. The main components of the system, which reflect a different perspective on the delivery of CIG advice that we call guideline as a service, are disclosed, and they include a health care Personal Assistant Web Application, a health care assistant mobile application, and the integration with the private calendar services of the user.(POCI-01-0145-)info:eu-repo/semantics/publishedVersio

Protégé: A Tool for Managing and Using Terminology in Radiology Applications

The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé (http://protege.stanford.edu) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé’s features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology