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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://sbi.postech.ac.kr/oasis/introduction/

A tool for various statistical tasks involved in analyzing survival data which provides a uniform platform to facilitate efficient statistical analyses of survival data in the aging field. The statistical features of OASIS include the calculation of Kaplan-Meier estimates, mean/median lifespan, mortality rate, Mantel-Cox Log-Rank test, Fishers exact test, weighted Log-Rank test, Kolmogorov-Smirnov test and Neymans smooth test. Moreover, OASIS generates survival and mortality curves that can be easily exported and modified by using common graphic softwares.

Proper citation: Online Application for Survival Analysis (OASIS) (RRID:SCR_014450) Copy   

•   Source: SciCrunch Registry

http://labs.europepmc.org/evf

A web application to assist in the identification of articles and research related to literature search terms. The search covers full text articles in the Europe PMC repository. Relevant papers are suggested to users based on the scientific term searched and the selection of questions, generated by the application, relevant to term searched.

Proper citation: EvidenceFinder (RRID:SCR_013764) Copy   

•   Source: SciCrunch Registry

http://neurolog.i3s.unice.fr/public_namespace/ontology

An ontology for neuroimaging or medical imaging studies based on DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. Detailed description from web: Our aim is the design of a common semantic model providing a unified view on all data and tools to be shared between NeuroLOG partners. For this purpose, we built a multi-layered and multi-components formal ontology. We chose a design framework that structures the ontology at different levels of abstraction while respecting common conceptualization choices. At the highest level is a top-level ontology that includes abstract concepts and relationships valid across domains. We adopted DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. We then added Core ontologies, which provide generic, basic and minimal concepts and relations in a specific domain. By minimal we mean that core ontologies should include only the most reusable and widely applicable categories. These kinds of ontologies are essential for sharing intended meaning between different domains. We adopted I& DA (Information and Discourse Acts), a core ontology initially built for classifying documents as a function of their content.We use it to model medical images, which we consider as types of documents. Participant Roles is the core ontology we use to describe the modes of image participation in data processing. I& DA and Participant Roles are built according to DOLCE ontological commitments. On the basis of these two layers, we constructed our Domain ontology dedicated to conceptualizing a specific domain, in this case neuroimaging. Obviously, large domains such as neuroimaging can be divided into sub-domains for the sake of modularization.

Proper citation: OntoNeuroLOG (RRID:SCR_008957) 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://www.cbs.dtu.dk/services/SignalP/

Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.

Proper citation: SignalP (RRID:SCR_015644) 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://dynamine.ibsquare.be/submission/

An NMR based method for protein folding prediction. Users can enter a UniProt identifier, FASTA sequences, or upload a file containing FASTA sequences and results are returned., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DynaMine (RRID:SCR_014559) 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

https://omictools.com/3omics-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 19, 2016. A web tool for visualizing and integrating multiple inter- or intra-transcriptomic, proteomic, and metabolomic human data. 3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for transcripts, proteins and metabolites.

Proper citation: 3Omics (RRID:SCR_014678) 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://www.people.fas.harvard.edu/~junliu/genotype/

Software application (entry from Genetic Analysis Software)

Proper citation: GS-EM (RRID:SCR_003992) Copy   

•   Source: SciCrunch Registry