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http://purl.bioontology.org/ontology/FB-DV

A structured controlled vocabulary of the development of Drosophila melanogaster.

Proper citation: Drosophila Development Ontology (RRID:SCR_010310) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/FB-BT

A structured controlled vocabulary of the anatomy of Drosophila melanogaster.

Proper citation: Drosophila Gross Anatomy Ontology (RRID:SCR_010311) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/FIX

Two ontologies: methods and properties (but not objects, which are subject of the chemical ontology). The methods are applied to study the properties.

Proper citation: Physico-Chemical Methods and Properties (RRID:SCR_010407) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/SSO

Ontology that encodes agreement among experts about how Emergency Department (ED) chief complaints are grouped into syndromes of public health importance (consensus definitions).

Proper citation: Syndromic Surveillance Ontology (RRID:SCR_010409) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ORDO

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).

Proper citation: Orphanet Rare Disease Ontology (RRID:SCR_010402) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PEO

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.

Proper citation: Parasite Experiment Ontology (RRID:SCR_010403) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PHARE

Ontology that proposes concepts and roles to represent relationships of pharmacogenomics interest.

Proper citation: Pharmacogenomic Relationships Ontology (RRID:SCR_010406) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/DDI

Ontology for the description of drug discovery investigations. DDI aims to follow to the OBO (Open Biomedical Ontologies) Foundry principles, uses relations laid down in the OBO Relation Ontology, and be compliant with Ontology for biomedical investigations (OBI).

Proper citation: Ontology for Drug Discovery Investigations (RRID:SCR_010383) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/OGSF

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.

Proper citation: Ontology for Genetic Susceptibility Factor (RRID:SCR_010386) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ACGT-MO

Ontology to represent the domain of cancer research and management in a computationally tractable manner.

Proper citation: Cancer Research and Management ACGT Master Ontology (RRID:SCR_006953) Copy   

•   Source: SciCrunch Registry

http://www.evidenceontology.org

A controlled vocabulary that describes types of scientific evidence within the realm of biological research that can arise from laboratory experiments, computational methods, manual literature curation, and other means. Researchers can use these types of evidence to support assertions about research subjects that result from scientific research, such as scientific conclusions, gene annotations, or other statements of fact. ECO comprises two high-level classes, evidence and assertion method, where evidence is defined as a type of information that is used to support an assertion, and assertion method is defined as a means by which a statement is made about an entity. Together evidence and assertion method can be combined to describe both the support for an assertion and whether that assertion was made by a human being or a computer. However, ECO can not be used to make the assertion itself; for that, one would use another ontology, free text description, or other means. ECO was originally created around the year 2000 to support gene product annotation by the Gene Ontology. Today ECO is used by many groups concerned with provenance in scientific research. ECO is used in AmiGO 2

Proper citation: ECO (RRID:SCR_002477) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/OntoVIP

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.

Proper citation: Medical image simulation (RRID:SCR_010355) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ONL-MSA

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).

Proper citation: Mental State Assessment (RRID:SCR_010357) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MSV

An ontology for metagenome sample metadata that mainly defines predicates.

Proper citation: Metagenome Sample Vocabulary (RRID:SCR_010358) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/TGMA

A structured controlled vocabulary of the anatomy of mosquitoes.

Proper citation: Mosquito Gross Anatomy Ontology (RRID:SCR_003839) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/CPTAC

A basic ontology which describes the proteomics pipeline infrastructure for CPTAC project

Proper citation: CPTAC Proteomics Pipeline Infrastructure Ontology (RRID:SCR_006945) Copy   

•   Source: SciCrunch Registry

http://bioportal.bioontology.org/annotator

A Web service that annotates textual metadata (e.g. journal abstract) with relevant ontology concepts. NCBO uses this Web service to annotate resources in the NCBO Resource Index. They also provide this Web service as a stand-alone service for users. This Web service can be accessed through BioPortal or used directly in your software. Currently, the annotation workflow is based on syntactic concept recognition (using concept names and synonyms) and on a set of semantic expansion algorithms that leverage the semantics in ontologies (e.g., is_a relations). Their service methodology leverages ontologies to create annotations of raw text and returns them using semantic web standards.

Proper citation: NCBO Annotator (RRID:SCR_005329) Copy   

•   Source: SciCrunch Registry

http://bmir.stanford.edu/

Mark Musen''s laboratory studies components for building knowledge-based systems, controlled terminologies and ontologies, and technology for the Semantic Web. For more than two decades, Musen''s group has worked to elucidate reusable building blocks of intelligent systems, and to develop scalable computational architectures for systems with significant applications in biomedicine. Informatics is the study of information: its structure, its communication, and its use. As society becomes increasingly information intensive, the need to understand, create, and apply new methods for modeling, managing, and acquiring information has never been greater especially in biomedicine. BMIR is home to world class scientists and trainees developing cutting-edge ways to acquire, represent, process, and manage knowledge and data related to health, health care, and the biomedical sciences. Our faculty, students, and staff are committed to ensuring the biomedical community is properly equipped for the information age, and believe our efforts will provide the structure for the burgeoning revolution of health care and the biomedical sciences.

Proper citation: Stanford Center for Biomedical Informatics Research (RRID:SCR_005698) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/sbo/

A set of controlled, relational vocabularies of terms commonly used in Systems Biology, and in particular in computational modeling. The ontology consists of seven orthogonal vocabularies defining: the roles of reaction participants (eg. substrate), quantitative parameters (eg. Michaelis constant), a precise classification of mathematical expressions that describe the system (eg. mass action rate law), the modeling framework used (eg. logical framework), and a branch each to describe entity (eg. macromolecule) and interaction (eg. process) types, and a branch to define the different types of metadata that may be present within a model. SBO terms can be used to introduce a layer of semantic information into the standard description of a model, or to annotate the results of biochemical experiments in order to facilitate their efficient reuse. SBO is an Open Biomedical Ontologies (OBO) candidate ontology, and is free for use. A programmatic access to the content of the Systems Biology Ontology is provided by Web Services.

Proper citation: SBO (RRID:SCR_006753) Copy   

•   Source: SciCrunch Registry

http://www.ncbcs.org/biositemaps/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 27,2023. Biositemaps represent a mechanism for computational biologists and bio-informaticians to openly broadcast and retrieve meta-data about biomedical data, tools and services (i.e., biomedical resources) over the Internet. All Institutions with an interest in biomedical research can publish a biositemap.rdf file on their Internet site. The technology, developed by the Biositemaps Working Group of the NIH Roadmap National Centers of Biomedical Computing (NCBC), addresses (i) locating, (ii) querying, (iii) composing or combining, and (iv) mining biomedical resources. Each site which intends to contribute to the inventory instantiates a file on its Internet site biositemap.rdf which conforms to a defined RDF schema and uses concepts from the Biomedical Resource Ontology to describe the resources. Each biositemap.rdf file is simply a list of controlled metadata about resources (software tools, databases, material resources) that your organization uses or believes are important to biomedical research. The key enabling technologies are the Information Model (IM) which is the list of metadata fields about each resource (resource_name, description, contact_person, resource_type,...) and the Biomedical Resource Ontology (BRO) which is a controlled terminology for the resource_typeand which is used to improve the sensitivity and specificity of web searches. Biositemaps blend the features of Sitemaps (enabling efficient web-content exploration) and RSS Feeds (a mechanism for wide and effective news dissemination). As a hybrid between Sitemaps and RSS feeds, the Biositemap infrastructure facilitates a decentralized, portable, extensible and computationally tractable generation and consumption of meta-data about existent, revised and new resources for biomedical computation. Web browsers, crawlers and robots can discover, accumulate, process, integrate and deliver Biositemaps content to (human or machine) users in a variety of graphical, tabular, computational formats. Biositemaps content allows such web browsers to pool resource-associated metadata from disparate and diverse sites and present it to the user in an integrated fashion. The Biositemaps protocol provides clues, information and directives for all Biositemap web harvesters that point to the existence and content of such biomedical resources at different sites.

Proper citation: Biositemaps (RRID:SCR_001976) Copy   

•   Source: SciCrunch Registry