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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.

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• SciCrunch
• 13 years ago - by Anonymous

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).

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• SciCrunch
• 13 years ago - by Anonymous

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.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that represents concepts related to homology, as well as other concepts used to describe similarity and non-homology.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology including the disease names, phenotypes and their classifications involved in Glucose Metabolism Disorder, Diabetes. (OBO and OWL format are available in sourceforge.)

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• SciCrunch
• 13 years ago - by Anonymous

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.

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• SciCrunch
• 13 years ago - by Anonymous

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.

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• SciCrunch
• 13 years ago - by Anonymous

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.

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• SciCrunch
• 13 years ago - by Anonymous

Terms associated with pediatrics, representing information related to child health and development from pre-birth through 21 years of age; contributed by the National Institute of Child Health and Human Development.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology for representing data mining investigations. Its goal is to allow the representation of knowledge discovery processes and be general enough to represent the data mining investigations. The ontology is based on the CRISP-DM process methodology.

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• SciCrunch
• 13 years ago - by Anonymous

Generic ontology for the domain of data mining that includes the information processing processes that occur in the domain of data mining, participants in the processes and their specifications. OntoDM is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. The generality in scope allows wide number of applications of the ontology, such as semantic annotation of data mining scenarios, ontology based support for QSARs, etc.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology to support systematic description of, and interoperable queries on, human studies and study elements.

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• SciCrunch
• 13 years ago - by Anonymous

A biomedical ontology in the domain of biological and clinical statistics that is primarily targeted for statistical representation in the fields in biological, biomedical, and clinical domains. It uses the Basic Formal Ontology (BFO) as the upper level ontology. OBCS imports all biostatistics related terms in the Ontology for Biomedical Investigations (OBI) including all logical axioms.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology for common concepts for communication between traditional medicine and western medicine. (In French)

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• SciCrunch
• 13 years ago - by Anonymous

Application ontology covering the domain of newborn screening, follow-up and translational research pertaining to patients diagnosed with inheritable and congenital diseases mainly identified through newborn dried blood spot screening. ONSTR is a central component of the project Newborn Screening Follow-up Data Integration Collaborative (NBSDC), https://nbsdc.org. ONSTR uses the Basic Formal Ontology v2 (BFO2, v2012-07-20) as top-level ontology and extends the classes imported from OBO Foundry ontologies and candidate ontologies.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology designed around the guiding concept of a symptom being: A perceived change in function, sensation or appearance reported by a patient indicative of a disease. Understanding the close relationship of Signs and Symptoms, where Signs are the objective observation of an illness, the Symptom Ontology will work to broaden it''s scope to capture and document in a more robust manor these two sets of terms. Understanding that at times, the same term may be both a Sign and a Symptom

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• SciCrunch
• 13 years ago - by Anonymous

Ontology to establish data exchange standards and common data elements in the microRNA (miR) domain. Biologists (cell biologists in particular) and bioinformaticians can make use of OMIT to leverage emerging semantic technologies in knowledge acquisition and discovery for more effective identification of important roles performed by miRs in humans'' various diseases and biological processes (usually through miRs'' respective target genes). OMIT has reused and extended a set of well-established concepts from existing bio-ontologies, e.g., Gene Ontology, Sequence Ontology, Protein Ontology, NCBI Organism Taxonomy, Human Disease Ontology, Foundational Model of Anatomy, and so forth.

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• SciCrunch
• 13 years ago - by Anonymous

Application ontology to model / represent the notion of genetic susceptibility to a specific disease or an adverse event or a pathological biological process. It is developed using BFO2.0''s framwork. The ontology is under the domain of genetic epidemiology.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology used to model the scientific investigation, especially Genome-Wide Association Study (GWAS), to find out genetic susceptibility factor to disease, such as Diabetes. It models the genetic varaints, polymorphisms, statistical measurement, populations and other elements that are essential to determine a genetic susceptibility factor in GWAS study. It must be used with other two ontologies, in the case of Diabetes, :Ontology of Geographical Region (OGR) and Ontology of Glucose Metabolism Disorder (OGMD) .

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• SciCrunch
• 13 years ago - by Anonymous

An ontology based on the papers Toward an Ontological Treatment of Disease and Diagnosis and On Carcinomas and Other Pathological Entities to address some of the issues raised at the Workshop on Ontology of Diseases (Dallas, TX) and the Signs, Symptoms, and Findings Workshop (Milan, Italy). OGMS was formerly called the clinical phenotype ontology. Terms from OGMS hang from the Basic Formal Ontology.

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• SciCrunch
• 13 years ago - by Anonymous