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Controlled vocabularies for the MIxS (Minimal Information about any Sequence) family of metadata checklists. See http://gensc.org/gc_wiki/index.php/MIxS for details on the MIxS checklists.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that provides a comprehensive description of the existing microbial typing methods for the identification of bacterial Isolates and their classification. Such a description constitutes an universal format for the exchange of information on the microbial typing field, providing a vehicle for the integration of the numerous disparate online databases. In its current version, TyPon describes most used microbial typing methods but it is, and always will be, a work in progress given the constant advances in the microbial typing field.

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• SciCrunch
• 13 years ago - by Anonymous

Structured controlled vocabulary for describing meta information of microbial calture collection maintained in biological research centers

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• SciCrunch
• 13 years ago - by Anonymous

An application ontology for microRNAs.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology for organismal habitats (especially focused on microbes)

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• SciCrunch
• 13 years ago - by Anonymous

An ontology for metagenome sample metadata that mainly defines predicates.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that is a module of the OntoNeuroLOG ontology that covers the field of mental state assessments, i.e. instruments, instrument variables, assessments, and resulting scores, developed in the context of the NeuroLOG project, a french project aiming at integrating distributed heterogeous resources in neuroimaging. It includes a generic domain core ontology, that provides a general model of such entities and a general taxonomy of behavioural, neurosychological and neuroclinical instruments, that can be easily extended to model any particular kind of instrument. It also includes such extensions for 8 relatively standard instruments, namely: (1) the Beck-depression-inventory-(BDI-II), (2) the Expanded-Disability-Status-Scale, (3) the Controlled-oral-word-association-test, (4) the Free-and-Cued-Selective-Reminding-Test-with-Immediate-Recall-16-item-version-(The-Grober-and-Buschke-test), (5) the Mini-Mental-State, (6) the Stroop-color-and-word-test, (7) the Trail-making-test-(TMT), (8) the Wechsler-Adult-Intelligence-Scale-third-edition, (9) the Clinical-Dementia-Rating-scale, (10) the Category-verbal-fluency, (11) the Rey-Osterrieth-Complex-Figure-Test-(CFT).

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• SciCrunch
• 13 years ago - by Anonymous

Ontology to (i) Provide better account of and better access to medical information through natural languages in order to help physicians in their daily practice, and to (ii) Enhance European cooperation by multilingual access to standardised medical nomenclatures. The major achievements of MENELAS are the realization of its two functional systems: (i) The Document Indexing System encodes free text PDSs into both an internal representation (a set of Conceptual Graphs) and international nomenclature codes (ICD-9-CM). Instances of the Document Indexing System have been realised for French, English and Dutch ; (ii) The Consultation System allows users to access the information contained in PDSs previously indexed by the Document Indexing System. The test domain for the project was coronary diseases. The existing prototype shows promising results for information retrieval from natural language PDSs and for automatically encoding PDSs into an existing classification such as ICD-9-CM. A set of components, tools, knowledge bases and methods has also been produced by the project. These include language-independent ontology and models for the domain of coronary diseases; conceptual description of the relevant ICD-9-CM codes. This ontology includes a top-ontology, a top-domain ontology and a domain ontology (Coronay diseases surgery). The menelas-top ontology here is the part of the whole ontology without any reference to medical domain.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that describes the content of the models used in medical image simulation developed in the context of the Virtual Imaging Platform project (VIP), a french project aiming at sharing medical image simulation resources. This ontology can be used to annotate such models in order to highlight the different entities that are present in the 3D scene to be imaged, i.e. anatomical structures, pathological structures, foreign bodies, contrast agents etc. The model allows also to associate to these entities information about their physical qualities, which are used in the medical image simulation process (to mimick physical phenomena involved in CT, MR, US and PET imaging). This ontology partly relies on the OntoNeuroLOG ontology (ONL-DP ONL-MR-DA), as well as PATO, RadLex, FMA and ChEBI.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that contains terms necessary for describing and categorizing concepts related to Major Histocompatibility Complex, in general, for a number of model species, and also for humans.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 23, 2014. Description not available.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology that is a module of the OntoNeuroLOG ontology, that covers the domain of Magnetic Resonance Imaging (MRI) dataset acquisition, i.e. MRI protocols, and MRI sequence parameters, developed in the context of the NeuroLOG project, a french project aiming at integrating distributed heterogeneous resources in neuroimaging. In particular, it includes a multi-axial classification of MR sequences.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology of the International Classification of Diseases Version 10, ICD-10-PCS (Procedure Coding System), 2009.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology of the International Classification of Diseases, 10th Edition, Clinical Modification, 2011_01

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• SciCrunch
• 13 years ago - by Anonymous

Ontology of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). 10th rev. Geneva, a medical classification list by the World Health Organization (WHO).

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• SciCrunch
• 13 years ago - by Anonymous

A system of classifications to enable systematic description of how injuries occur. It is designed especially to assist injury prevention. It was originally designed for use in settings in which information is recorded in a way that allows statistical reporting--for example, injury surveillance based on collection of information about cases attending a sample of hospital emergency departments. It has also been found useful for other purposes. For example, it has been used as a reference classification during revision of another classification, to record risk-factor exposure of children in a cohort study, as the basis for special-purpose classifications and in a growing number of other ways.

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• SciCrunch
• 13 years ago - by Anonymous

An ontology in the domain of interaction network that aims to standardize interaction network annotation, integrate various interaction network data, and support computer-assisted reasoning. It is aimed to represent general interactions (e.g., molecular interactions) and interaction networks (e.g., Bayesian network). INO was initiated by supporting literature mining related to interactions and interaction networks. INO aligns with BFO. INO is a community-based ontology, and its development follows the OBO Foundry principles.

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• SciCrunch
• 13 years ago - by Anonymous

A custom-built terminology to describe the nanomanufacturing enterprise.

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• SciCrunch
• 13 years ago - by Anonymous

Ontologies designed as a set of interoperable ontologies that will together provide coverage of the infectious disease domain. At the core of the set is a general Infectious Disease Ontology (IDO-Core) of entities relevant to both biomedical and clinical aspects of most infectious diseases. Sub-domain specific extensions of IDO-Core complete the set providing ontology coverage of entities relevant to specific pathogens or diseases. Please note: The ontology metrics displayed by BioPortal do not distinguish IDO-developed terms from terms imported from other ontologies.

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• SciCrunch
• 13 years ago - by Anonymous

Ontology generated as part of the Bioinformatics Integration Support Contract (BISC) that is based on the National Library of Medicine (NLM) Medical Subject Headings; National Cancer Institute Thesaurus; International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM); ICD-10; and other open source public databases. Specific information may be available about a class, including Preferred_Name, DEFINITION, Synonym, etc.

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• SciCrunch
• 13 years ago - by Anonymous