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http://purl.bioontology.org/ontology/NEOMARK4

Ontology that describes the medical information necessary for early detection of the oral cancer reoccurrence extracted from the NeoMark Project.

Proper citation: Neomark Oral Cancer Ontology version 4 (RRID:SCR_010376) Copy   

•   Source: SciCrunch Registry

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

Ontology of neural functional motor recovery.

Proper citation: Neural Motor Recovery Ontology (RRID:SCR_010377) Copy   

•   Source: SciCrunch Registry

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

Species ontology that adopts and integrates relevant portions of available taxonomies as needed based on the species and strain terms represented in the current release of NeuroMorpho.Org (72 terms as of the 5.7 release) and any future additions. When a NeuroMorpho.Org term is mapped with an external resource, its entire lineage (ancestors and descendants) is added to the NeuroMorpho.Org species ontology. The resulting 1,340 terms of this initial version of the ontology come for 65% from the NCBI taxonomy (24 NeuroMorpho.Org species/strain terms mapped), 30% from the Rat Gene Database (1 term mapped), and altogether 5% from NIFSTD (7 terms mapped), MeSH (2 terms mapped), ITIS (1 term mapped), and custom-added concepts (41 terms mapped, largely mouse strains from Jackson Labs).

Proper citation: NeuroMorpho.Org species ontology (RRID:SCR_010378) Copy   

•   Source: SciCrunch Registry

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

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.

Proper citation: Protein Modification Ontology (RRID:SCR_010412) Copy   

•   Source: SciCrunch Registry

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

Collection of ontologies that define the base classes properties, and restrictions used for modeling physical quantities, units of measure, and their dimensions in various measurement systems. The goal of the QUDT ontology is to provide a unified model of, measurable quantities, units for measuring different kinds of quantities, the numerical values of quantities in different units of measure and the data structures and data types used to store and manipulate these objects in software. This OWL schema is a foundation for a basic treatment of units.

Proper citation: QUDT (RRID:SCR_010416) Copy   

•   Source: SciCrunch Registry

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

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.

Proper citation: Ontology for MicroRNA Target Prediction (RRID:SCR_010387) Copy   

•   Source: SciCrunch Registry

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

Ontology in the domain of role classification that aims to standardize role classification and support computer-assisted reasoning. RoleO is a community-based ontology, and its development follows the OBO Foundry principles.

Proper citation: Role Ontology (RRID:SCR_010420) Copy   

•   Source: SciCrunch Registry

http://uberon.org

An integrated cross-species anatomy ontology representing a variety of entities classified according to traditional anatomical criteria such as structure, function and developmental lineage. The ontology includes comprehensive relationships to taxon-specific anatomical ontologies, allowing integration of functional, phenotype and expression data. Uberon consists of over 10000 classes (March 2014) representing structures that are shared across a variety of metazoans. The majority of these classes are chordate specific, and there is large bias towards model organisms and human.

Proper citation: UBERON (RRID:SCR_010668) Copy   

•   Source: SciCrunch Registry

http://bioassayontology.org/

Ontology to describe and categorize chemical biology and drug screening assays and their results including high-throughput screening (HTS) data for the purpose of categorizing assays and data analysis. BAO is an extensible, knowledge-based, highly expressive (currently SHOIQ(D)) description of biological assays making use of descriptive logic based features of the Web Ontology Language (OWL). BAO currently has over 700 classes and also makes use of several other ontologies. It describes several concepts related to biological screening, including Perturbagen, Format, Meta Target, Design, Detection Technology, and Endpoint. Perturbagens are perturbing agents that are screened in an assay; they are mostly small molecules. Assay Meta Target describes what is known about the biological system and / or its components interrogated in the assay (and influenced by the Perturbagen). Meta target can be directly described as a molecular entity (e.g. a purified protein or a protein complex), or indirectly by a biological process or event (e.g. phosphorylation). Format describes the biological or chemical features common to each test condition in the assay and includes biochemical, cell-based, organism-based, and variations thereof. The assay Design describes the assay methodology and implementation of how the perturbation of the biological system is translated into a detectable signal. Detection Technology relates to the physical method and technical details to detect and record a signal. Endpoints are the final HTS results as they are usually published (such as IC50, percent inhibition, etc). BAO has been designed to accommodate multiplexed assays. All main BAO components include multiple levels of sub-categories and specification classes, which are linked via object property relationships forming an expressive knowledge-based representation.

Proper citation: Bioassay Ontology (RRID:SCR_002638) Copy   

•   Source: SciCrunch Registry

http://www.geneontology.org/

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

Proper citation: Gene Ontology (RRID:SCR_002811) 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

https://github.com/obophenotype/porifera-ontology

An ontology covering the anatomy of Porifera (sponges).

Proper citation: Porifera Ontology (RRID:SCR_000134) Copy   

•   Source: SciCrunch Registry

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

Ontology for representing events, time, and their relationships.

Proper citation: Time Event Ontology (RRID:SCR_000310) Copy   

•   Source: SciCrunch Registry

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

An ontology for standardized, systematic description of effects, consequences and mechanisms of variations.

Proper citation: Variation Ontology (RRID:SCR_000311) Copy   

•   Source: SciCrunch Registry

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

An ontology for DICOM as used in the SeDI project.

Proper citation: Semantic DICOM Ontology (RRID:SCR_000309) Copy   

•   Source: SciCrunch Registry

http://www.xenbase.org/anatomy/xao.do?method=display

A structured, controlled vocabulary of the anatomy and development of the African clawed frogs (Xenopus laevis and tropicalis), organized in a graphical structure. Tissues are shown as being part of other tissues and the timing of their development is indicated by start and end stages. The lineage of tissues is represented by develops from relationships between different tissues at different developmental stages. Many items have been classified according to the Common Anatomy Reference Ontology. The Xenopus Anatomical Ontology will be used to annotate Xenopus gene expression patterns and mutant and morphant phenotypes. Its robust developmental map will enable powerful database searches and data analyses. They encourage community recommendations for updates and improvements to the ontology.

Proper citation: Xenopus Anatomy Ontology (RRID:SCR_004337) Copy   

•   Source: SciCrunch Registry

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

An ontology to describe the entities and relationships in the SEEK database, a Systems Biology environment for the sharing and exchange of data and models. The SysMO-SEEK database contains the work of the SysMO consortium (Systems Biology of Micro-Organisms) https://seek.sysmo-db.org/

Proper citation: SysMO JERM Ontology of Systems Biology for Micro-Organisms (RRID:SCR_004569) Copy   

•   Source: SciCrunch Registry

http://www.proconsortium.org/pro/

An ontological representation of protein-related entities by explicitly defining them and showing the relationships between them. Each PRO term represents a distinct class of entities (including specific modified forms, orthologous isoforms, and protein complexes) ranging from the taxon-neutral to the taxon-specific. The ontology has a meta-structure encompassing three areas: proteins based on evolutionary relatedness (ProEvo); protein forms produced from a given gene locus (ProForm); and protein-containing complexes (ProComp). NOTICE: The PRO ID format has changed from PRO: to PR: (e.g. PRO:000000563 is now PR:000000563).

Proper citation: PR (RRID:SCR_004964) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/GRO-CPD

A structured controlled vocabulary for describing cereal plant development and growth stages. Please note that this ontology has now been superseded by the Plant Ontology.

Proper citation: Cereal Plant Development Ontology (RRID:SCR_005095) 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