We support boolean queries, use +,-,<,>,~,* to alter the weighting of terms
Ontology that expresses the PROV Data Model using the OWL2 Web Ontology Language (OWL2) providing a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains.
A comprehensive proteomics data and process provenance ontology.
A structured controlled vocabulary for the annotation of experiments concerned with protein-protein interactions.
Ontology consisting of terms that describe protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified.
Ontology of a system for tracking grants and producing reports - Users can access grant data through a query interface and a variety of pre-defined forms and reports.
A controlled vocabulary of growth and developmental stages in various plants. Note that this has been subsumed into the Plant Ontology (PO). This file is created by filtering plant_ontology_assert.obo to contain only terms from the plant structure development stage branch of the PO. For more information, please see: http://palea.cgrb.oregonstate.edu/viewsvn/Poc/tags/live/
Ontology that encodes agreement among experts about how Emergency Department (ED) chief complaints are grouped into syndromes of public health importance (consensus definitions).
THIS RESOURCES IS NO LONGER IN SERVICE, documented on April 23, 2014. REPLACED BY: Plant Ontology (PO). A controlled vocabulary of plant morphological and anatomical structures representing organs, tissues, cell types, and their biological relationships based on spatial and developmental organization. Note that this has been subsumed into the PO. This file is created by filtering plant_ontology_assert.obo to contain only terms from the plant anatomical entity branch of the PO. For more information, please see: http://palea.cgrb.oregonstate.edu/viewsvn/Poc/tags/live/
Two ontologies: methods and properties (but not objects, which are subject of the chemical ontology). The methods are applied to study the properties.
Ontology that proposes concepts and roles to represent relationships of pharmacogenomics interest.
An ontology for describing both human infectious disease caused by bacteria and the disease that is related to bacterial infection.
Vocabulary that describes a process that is the means of how a pathogen is transmitted from one host, reservoir, or source to another host. This transmission may occur either directly or indirectly and may involve animate vectors or inanimate vehicles.
Ontology that models provenance metadata associated with experiment protocols used in parasite research. The PEO extends the upper-level Provenir ontology (http://knoesis.wright.edu/provenir/provenir.owl) to represent parasite domain-specific provenance terms. The PEO (v 1.0) includes Proteome, Microarray, Gene Knockout, and Strain Creation experiment terms along with other terms that are used in pathway.
Ontology to provide a structured vocabulary for rare diseases capturing relationships between diseases, genes and other relevant features which will form a useful resource for the computational analysis of rare diseases. It derived from the Orphanet database (http://www.orpha.net) , a multilingual database dedicated to rare diseases populated from literature and validated by international experts. It integrates a nosology (classification of rare diseases), relationships (gene-disease relations, epiemological data) and connections with other terminologies (MeSH, SNOMED CT, UMLS, MedDRA), databases (OMIM, UniProtKB, HGNC, ensembl, Reactome, IUPHAR, Geantlas) or classifications (ICD10). The ontology will be maintained by Orphanet and further populated with new data. Orphanet classifications can be browsed in the OLS view. The Orphanet Rare Disease Ontology is updated monthly and follows the OBO guidelines on deprecation of terms. It constitutes the official ontology of rare diseases produced and maintained by Orphanet (INSERM, US14).
Ontology of language terms used in the domain of autism available for consultation and sharing. The language terms were obtained via text mining and automatic retrieval of terms from the corpus of PubMed abstracts.
Ontology that represents concepts related to homology, as well as other concepts used to describe similarity and non-homology.
Ontology including the disease names, phenotypes and their classifications involved in Glucose Metabolism Disorder, Diabetes. (OBO and OWL format are available in sourceforge.)
Ontology that is used with other ontologies to represent the genetic susceptibility factors of diabetes. This OWL ontology classified the geograhical regions related vocabularies extracted from UMLS.
Ontology that contains entities such as: datatype, datatype generator, datatype quality and others giving the possibility to represent arbitrary complex datatypes. This is an important fact for a general data mining ontology that wants to represent and query over modelling algorithms for mining structured data. The ontology was first developed under the OntoDM (Ontology of Data Mining, http://kt.ijs.si/panovp/OntoDM) ontology, but for generality and reuse purpose it was decided to export it as a separate ontology. Additionaly, the OntoDT ontology is based on and ISO/IEC 11404 (http://www.iso.org/iso/catalogue_detail.htm?csnumber=39479) standard and can be reused used independently by any domain ontology that requires representation and reasoning about general purpose datatypes.
Ontology that provides a lightweight representation of the variables used to measure experimental properties and the measurement scales that form the complex data types supporting that data. Many different variables measure the same thing, here they use a lightweight representation driven by a small number of classes and a large number of variables to focus only on providing a vocabulary of variables that may be extended for consolidation to standardized variables for specific things and functions to map between values from different measurements scales. They use the base ontology description to provide a very lightweight representation of the basic elements of an experimental design and they use views to instantiate it for specific domains.