HCLSIG BioRDF Subgroup/Tasks/Vocabulary Requirements
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Vocab: Immunology Example:
NLP will make use of a standard vocabulary to tag relevant terms in text documents. These are probably going to be on clinical aspects of neurological diseases, therefore a vocabulary covering this domain should be preferred. The UMLS metathesaurus could be a good choice for nlp. As the nlp engine will assign SNOMED-CT codes to terms, the mapping between UMLS and SNOMED-CT should be addressed.
Vocabulary for the task: Ligand-Receptor Interaction, Molecular Interaction Networks, Ontology Evolution The task uses data from two data sources: the BIND (Biomolecular Interaction Network Database) and the PDSP KiDB. It also contains some additional mappings to other vocabularies.
- The (neuro-)receptor molecules from the KiDB are classified with the IUHPAR nomeclature of receptors (certain receptor types are collected in classes of receptors). I do not know if an equivalent to the IUPHAR-DB would exist in the UMLS
- The receptors from the KiDB are annotated with terms from Entrez Taxonomy (which is part of UMLS, to my knowledge)
- The ligands in the KiDB are annotated with their respective CAS numbers (maybe these could be mapped to Chebi, which is a part of the UMLS)
- The data form BIND contains many different kinds of molecules (small molecules, proteins, RNA, DNA).
Using SW Technologies to Find Small Molecules that Bind to Proteins No vocabularies or ontologies are required outside of the sets of identifiers used by the relevant databases. These sets include but are not limited to the set of all domain identifiers in SMART and the set of all compound identifiers in SMID or PubChem (PubChem Substance).