PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Ontology”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2006

A top-level ontology of functions and its application in the Open Biomedical Ontologies.

MOTIVATION: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. RESULTS: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

Biomedical Engineering↗
PubMed2006

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins. The OAT system consists of two disjointed parts; a MySQL database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries. The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System. We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at: http://www.ifm.liu.se/bioinfo/oat.

Algorithms↗
PubMed2004

Inflammation ontology design pattern: an exercise in building a core biomedical ontology with descriptions and situations.

Formal ontology has proved to be an extremely useful tool for negotiating intended meaning, for building explicit, formal data sheets, and for the discovery of novel views on existing data structures. This paper describes an example of application of formal ontological methods to the creation of biomedical ontologies. Addressed here is the ambiguous notion of inflammation, which spans across multiple linguistic meanings, multiple layers of reality, and multiple details of granularity. We use UML class diagrams, description logics, and the DOLCE foundational ontology, augmented with the Description and Situation theory, in order to provide the representational and ontological primitives that are necessary for the development of detailed, flexible, and functional biomedical ontologies. An ontology design pattern is proposed as a modelling template for inflammations.

Humans↗
PubMed2006

Aligning biomedical ontologies using lexical methods and the UMLS: the case of disease ontologies.

The process of aligning ontologies comprises two major steps: i) mapping concepts and ii) characterizing the relations between the concepts. In this paper, we present an alignment method based on a hybrid approach that reuses the UMLS knowledge base and aims at identifying patterns to characterize the relations. The proposed method consist in four steps: 1) exact matching, 2) searching for terms from one ontology that are included in terms from the other ontology, 3) identifying direct relations through the UMLS and 4) extracting syntactico-semantic patterns to infer novel alignments. This method has been applied to aligning the Human Disease ontology and the Mouse Pathology ontology resulting in 48 exact matches and 3,697 pairs of concepts for which one term is included in a term from the other ontology. 1,270 alignments are present in the UMLS. Among these, 903 are characterized by a semantic attribute. Based on these alignments, a study of the syntactic patterns has been done. Not surprisingly, the distribution of the different syntactic patterns is not sufficient to discriminate the different types of relationships found in the UMLS alignments. We have used the semantic categorization of the concepts provided by the UMLS to extract syntactico-semantic patterns. 87 novel alignments based on 6 syntactico-semantic patterns associated with isa and has associated morphology have been inferred.

Animals↗
PubMed2005

A novel view on information content of concepts in a large ontology and a view on the structure and the quality of the ontology.

Semantic distance and semantic similarity are two important information retrieval measures used in word sense disambiguation as well as for the assessment of how relevant concepts are with respect to the documents in which they are found. A variety of calculation methods have been proposed in the literature, whereby methods taking into account the information content of an individual concept outperform those that do not. In this paper, we present a novel recursive approach to calculate a concept's information content based on the information content of the concepts to which it relates. The method is applicable to extremely large ontologies containing several million concepts and relationships amongst them. It is shown that a concept's information content as calculated by this method provides additional information with respect to an ontology that cannot be approximated by hierarchical edge-counting or human insight. In addition, it is suggested that the method can be used for quality control within large ontologies and that it can give you an impression on the structure and the quality of the ontology.

Algorithms↗
PubMed1999

Ontology for immunogenetics: the IMGT-ONTOLOGY.

MOTIVATION: IMGT, the international ImMunoGeneTics database (http:@imgt.cines.fr:8104), created by M.-P. Lefranc, is an integrated database specializing in antigen receptors (immunoglobulins and T-cell receptors) and major histocompatibility complex (MHC) of all vertebrate species. IMGT accurate immunogenetics data are based on the standardization of the biological knowledge provided by the 'ImMunoGeneTics' IMGT-ONTOLOGY. The IMGT-ONTOLOGY describes the classification and specification of terms needed for immunogenetics and bioinformatics. IMGT-ONTOLOGY covers four main concepts: 'IDENTIFICATION', 'DESCRIPTION', 'CLASSIFICATION' and 'OBTENTION'. These concepts allow an extensive and standardized description and characterization of immunoglobulin and T-cell receptor data. The controlled vocabulary and the annotation rules are indispensable to ensure accuracy, consistency and coherence in IMGT. IMGT-ONTOLOGY allows scientists and clinicians to use, for the first time, identical terms with the same meaning in immunogenetics. It provides a semantic repository that will improve interoperability between specialist and generalist databases.

Animals↗
PubMed2006

Event ontology: a pathway-centric ontology for biological processes.

Event ontology is a new biomedical ontology developed to annotate pathway components in a pathway database. It organizes the concepts and terms of sub-pathways, pathways, biological phenomena, experimental conditions, medications, and external stimuli appearing in biological pathways (e.g. signal transduction, disease-, metabolic-, molecular interaction-, genetic interaction pathways, etc.). Concepts in the Event ontology are extracted manually from scientific literature. Each term has links to external databases such as Gene Ontology, Reactome, KEGG, BioCyc, and PubMed.

Computational Biology↗
PubMed2007

Linked gene ontology categories are novel and differ from associated gene ontology categories for the bipolar disorders.

Family, and twin genetic studies strongly indicate gene variants as predisposing to the bipolar disorders. Now, about 3000 genes are genetically linked and about 44 genes are genetically associated. Rank differences, however, exist between the linked gene Genetic ontology categories and the associated gene Genetic ontology categories. For the linked gene Genetic ontology categories, the activation of NF-kappaB-inducing kinase category is over-represented; in contrast, the associated genes show the Synaptic transmission category as over-represented. Association studies report selecting positional candidate genes from previous linkage studies, or, selecting genes on the basis of pathophysiologic hypotheses. Only a few of the pathophysiologic hypotheses genes, however, had been previously linked to the bipolar disorders. In particular, only a couple of the Synaptic transmission genes had been previously linked to bipolar disorders.

Bipolar Disorder↗
PubMed2004

Ontologizing gene-expression microarray data: characterizing clusters with Gene Ontology.

An XML-based Java application is described that provides a function-oriented overview of the results of cluster analysis of gene-expression microarray data based on Gene Ontology terms and associations. The application generates one HTML page with listings of the frequencies of explicit and implicit Gene Ontology annotations for each cluster, and separate, linked pages with listings of explicit annotations for each gene in a cluster.

Cluster Analysis↗
PubMed2004

The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.

Animals↗
PubMed2006

GOFFA: gene ontology for functional analysis--a FDA gene ontology tool for analysis of genomic and proteomic data.

BACKGROUND: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated. RESULTS: Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions. CONCLUSION: With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses. AVAILABILITY: GOFFA is available through ArrayTrack softwarehttp://edkb.fda.gov/webstart/arraytrack/.

Genomics↗
PubMed2003

The ontology of the gene ontology.

The rapidly increasing wealth of genomic data has driven the development of tools to assist in the task of representing and processing information about genes, their products and their functions. One of the most important of these tools is the Gene Ontology (GO), which is being developed in tandem with work on a variety of bioinformatics databases. An examination of the structure of GO, however, reveals a number of problems, which we believe can be resolved by taking account of certain organizing principles drawn from philosophical ontology. We shall explore the results of applying such principles to GO with a view to improving GO's consistency and coherence and thus its future applicability in the automated processing of biological data.

Computational Biology↗
PubMed2005

Building a Hospital Incident Reporting Ontology (HIRO) in the Web Ontology Language (OWL) using the JCAHO Patient Safety Event Taxonomy (PSET).

A Hospital Incident Reporting Ontology (HIRO) is being developed in Protégé-OWL to demonstrate feasibility and clinical value of using an ontology to combine, compare, and analyze data from across many public and private reporting systems collecting adverse events and near misses for patient safety. The HIRO is based on the JCAHO Patient Safety Event Taxonomy (PSET) and de-identified hospital incident reports.

Humans↗
PubMed2006

[From ontological compartimentalisation to ontological complexity].

Discussions about the foundations of psychiatry show repetitive traits. Although in the disputes that arise between the three mainframes of reference (i.e. physical/naturalistic, hermeneutic-phenomenological, and socio-critical) new ideas do emerge with regard to content, hardly any changes can be detected in the basic hypotheses. In this article this stalemate is analysed in terms of the fundamental ontological theories that lie at the root of the three perspectives. An attempt is made to drive the debate forwards by the introduction of the concept of ontological complexity. Eventually it is argued that existential and moral learning processes of the psychiatrist (and in the environment of their patients) are just as important for their profession as are empirical and analytical insights, measurement scales and methods and innumerable pharmacological aids.

Brain↗
PubMed2004

GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes.

SUMMARY: GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology (GO) information and evaluating and visualizing the collective annotation of a list of genes to GO terms. It can be used to draw conclusions from microarray and other biological data, calculating the statistical significance of each annotation. GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. AVAILABILITY: The full source code and documentation for GO::TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/.

Abstracting and Indexing↗
PubMed2002

On ontologies for biologists: the Gene Ontology--untangling the web.

The mantra of the 'post-genomic' era is 'gene function'. Yet surprisingly little attention has been given to how functional and other information concerning genes is to be captured, made accessible to biologists or structured in a computable form. The aim of the Gene Ontology (GO) Consortium is to provide a framework for both the description and the organisation of such information. The GO Consortium is presently concerned with three structured controlled vocabularies which can be used to describe three discrete biological domains, building structured vocabularies which can be used to describe the molecular function, biological roles and cellular locations of gene products.

Animals↗