2,412 research outputs found
Infectious Disease Ontology
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
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
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
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 âŒ900 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
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 NIFâs 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
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
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 tùche d'alignement local est automatiquement alignée en se basant sur les techniques d'apprentissage automatique. Un classificateur local aligne une seule tùche d'alignement local. Les classificateurs locaux sont basés sur des features élémentaires et structurelles. L'attribut class de chaque set de donne d'apprentissage " training set" est automatiquement étiqueté à l'aide d'une base de connaissances externe. Nous avons appliqué une technique de sélection de features pour chaque classificateur local afin de sélectionner les matchers appropriés pour chaque tùche d'alignement local. Cette approche réduit la complexité d'alignement et augmente la précision globale par rapport aux méthodes d'apprentissage traditionnelles. Nous avons prouvé que l'approche de partitionnement est meilleure que les approches actuelles en terme de précision, de taux de couverture et d'absence de partitions isolées. Nous avons évalué l'approche d'apprentissage d'alignement local à l'aide de diverses expériences basées sur des jeux de données d'OAEI 2018. Nous avons déduit qu'il est avantageux de diviser une grande tùche d'alignement d'ontologies en un ensemble de tùches d'alignement locaux. L'espace de recherche est réduit, ce qui réduit le nombre de faux négatifs et de faux positifs. L'application de techniques de sélection de caractéristiques à chaque classificateur local augmente la valeur de rappel pour chaque tùche 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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