2,412 research outputs found

    Infectious Disease Ontology

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    Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

    A Query Integrator and Manager for the Query Web

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    We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

    In Silico Approaches and the Role of Ontologies in Aging Research

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    The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin

    CellFinder: a cell data repository

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    CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∌900 proteins and genes. CellFinder's data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder's web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians

    Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

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    An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies

    WormBase 2007

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    WormBase (www.wormbase.org) is the major publicly available database of information about Caenorhabditis elegans, an important system for basic biological and biomedical research. Derived from the initial ACeDB database of C. elegans genetic and sequence information, WormBase now includes the genomic, anatomical and functional information about C. elegans, other Caenorhabditis species and other nematodes. As such, it is a crucial resource not only for C. elegans biologists but the larger biomedical and bioinformatics communities. Coverage of core areas of C. elegans biology will allow the biomedical community to make full use of the results of intensive molecular genetic analysis and functional genomic studies of this organism. Improved search and display tools, wider cross-species comparisons and extended ontologies are some of the features that will help scientists extend their research and take advantage of other nematode species genome sequences

    Local matching learning of large scale biomedical ontologies

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    Les larges ontologies biomĂ©dicales dĂ©crivent gĂ©nĂ©ralement le mĂȘme domaine d'intĂ©rĂȘt, mais en utilisant des modĂšles de modĂ©lisation et des vocabulaires diffĂ©rents. Aligner ces ontologies qui sont complexes et hĂ©tĂ©rogĂšnes est une tĂąche fastidieuse. Les systĂšmes de matching doivent fournir des rĂ©sultats de haute qualitĂ© en tenant compte de la grande taille de ces ressources. Les systĂšmes de matching d'ontologies doivent rĂ©soudre deux problĂšmes: (i) intĂ©grer la grande taille d'ontologies, (ii) automatiser le processus d'alignement. Le matching d'ontologies est une tĂąche difficile en raison de la large taille des ontologies. Les systĂšmes de matching d'ontologies combinent diffĂ©rents types de matcher pour rĂ©soudre ces problĂšmes. Les principaux problĂšmes de l'alignement de larges ontologies biomĂ©dicales sont: l'hĂ©tĂ©rogĂ©nĂ©itĂ© conceptuelle, l'espace de recherche Ă©levĂ© et la qualitĂ© rĂ©duite des alignements rĂ©sultants. Les systĂšmes d'alignement d'ontologies combinent diffĂ©rents matchers afin de rĂ©duire l'hĂ©tĂ©rogĂ©nĂ©itĂ©. Cette combinaison devrait dĂ©finir le choix des matchers Ă  combiner et le poids. DiffĂ©rents matchers traitent diffĂ©rents types d'hĂ©tĂ©rogĂ©nĂ©itĂ©. Par consĂ©quent, le paramĂ©trage d'un matcher devrait ĂȘtre automatisĂ© par les systĂšmes d'alignement d'ontologies afin d'obtenir une bonne qualitĂ© de correspondance. Nous avons proposĂ© une approche appele "local matching learning" pour faire face Ă  la fois Ă  la grande taille des ontologies et au problĂšme de l'automatisation. Nous divisons un gros problĂšme d'alignement en un ensemble de problĂšmes d'alignement locaux plus petits. Chaque problĂšme d'alignement local est indĂ©pendamment alignĂ© par une approche d'apprentissage automatique. Nous rĂ©duisons l'Ă©norme espace de recherche en un ensemble de taches de recherche de corresondances locales plus petites. Nous pouvons aligner efficacement chaque tache de recherche de corresondances locale pour obtenir une meilleure qualitĂ© de correspondance. Notre approche de partitionnement se base sur une nouvelle stratĂ©gie Ă  dĂ©coupes multiples gĂ©nĂ©rant des partitions non volumineuses et non isolĂ©es. Par consĂ©quence, nous pouvons surmonter le problĂšme de l'hĂ©tĂ©rogĂ©nĂ©itĂ© conceptuelle. Le nouvel algorithme de partitionnement est basĂ© sur le clustering hiĂ©rarchique par agglomĂ©ration (CHA). Cette approche gĂ©nĂšre un ensemble de tĂąches de correspondance locale avec un taux de couverture suffisant avec aucune partition isolĂ©e. Chaque tĂąche d'alignement local est automatiquement alignĂ©e en se basant sur les techniques d'apprentissage automatique. Un classificateur local aligne une seule tĂąche d'alignement local. Les classificateurs locaux sont basĂ©s sur des features Ă©lĂ©mentaires et structurelles. L'attribut class de chaque set de donne d'apprentissage " training set" est automatiquement Ă©tiquetĂ© Ă  l'aide d'une base de connaissances externe. Nous avons appliquĂ© une technique de sĂ©lection de features pour chaque classificateur local afin de sĂ©lectionner les matchers appropriĂ©s pour chaque tĂąche d'alignement local. Cette approche rĂ©duit la complexitĂ© d'alignement et augmente la prĂ©cision globale par rapport aux mĂ©thodes d'apprentissage traditionnelles. Nous avons prouvĂ© que l'approche de partitionnement est meilleure que les approches actuelles en terme de prĂ©cision, de taux de couverture et d'absence de partitions isolĂ©es. Nous avons Ă©valuĂ© l'approche d'apprentissage d'alignement local Ă  l'aide de diverses expĂ©riences basĂ©es sur des jeux de donnĂ©es d'OAEI 2018. Nous avons dĂ©duit qu'il est avantageux de diviser une grande tĂąche d'alignement d'ontologies en un ensemble de tĂąches d'alignement locaux. L'espace de recherche est rĂ©duit, ce qui rĂ©duit le nombre de faux nĂ©gatifs et de faux positifs. L'application de techniques de sĂ©lection de caractĂ©ristiques Ă  chaque classificateur local augmente la valeur de rappel pour chaque tĂąche d'alignement local.Although a considerable body of research work has addressed the problem of ontology matching, few studies have tackled the large ontologies used in the biomedical domain. We introduce a fully automated local matching learning approach that breaks down a large ontology matching task into a set of independent local sub-matching tasks. This approach integrates a novel partitioning algorithm as well as a set of matching learning techniques. The partitioning method is based on hierarchical clustering and does not generate isolated partitions. The matching learning approach employs different techniques: (i) local matching tasks are independently and automatically aligned using their local classifiers, which are based on local training sets built from element level and structure level features, (ii) resampling techniques are used to balance each local training set, and (iii) feature selection techniques are used to automatically select the appropriate tuning parameters for each local matching context. Our local matching learning approach generates a set of combined alignments from each local matching task, and experiments show that a multiple local classifier approach outperforms conventional, state-of-the-art approaches: these use a single classifier for the whole ontology matching task. In addition, focusing on context-aware local training sets based on local feature selection and resampling techniques significantly enhances the obtained results
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