![]() The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. To provide the community with a resource free of these hurdles, we have developed DisGeNET (), one of the largest available collections of genes and variants involved in human diseases. ![]() However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. To assist in this complex task, we created DisGeNET (), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. RDF queries benefit from page-level compression that reduces I/O, and the Oracle cost based optimizer and hints to maximize performance.One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease.Inferencing, including incremental and parallel modes, is done in advance of queries and persisted for scalability and faster querying.Reduces storage requirements by over 60% with page-level compression and efficient storage that eliminates RDF URI redundancy. Employs SQL*Loader direct-path loading and partitioning.Semantic Indexing finds documents of interest stored in Oracle Database and works with 3rd party entity extraction services, such as OpenCalais and GATE.Secures RDF data at the graph and/or the triple level with declarative constraints on RDF classes and properties or EAL4+ multi-level security and mandatory access control.Supports key open source technologies, including Jena, Joseki, ARQ, Sesame, Pellet, D2RQ, Jetty, Cytoscape, GATE, and Protégé.Supports W3C standards for the representation, vocabulary, inferencing and querying of relationships, including RDF, SKOS, RDFS, OWL, and SPARQL.Queries relationship patterns using either direct SPARQL access to Oracle Database 11g via Jena, Sesame or Joseki, or SQL with SPARQL-like clauses.Inferred data is persisted in the database for faster querying. Enables machine-driven discovery of new relationships using the native Oracle Database inference engine, ontologies, and RDFS /SKOS/OWL semantics and user defined rules.Stores rich, real-world relationships in the data beyond columns, table joins and Boolean to obtain more semantically complete query results.Based on a graph data model, RDF data (triples) are persisted, indexed and queried, like other object-relational data "Oracle Database 11g Semantic Technologies is an open, standards-based, scalable, secure, reliable and performant RDF management platform. Will SQL server support semantic standards such as RDF, OWL, SPARQL similarly to Oracle 11g? Thank you.
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