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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 3.64(USD Billion) |
MARKET SIZE 2024 | 4.17(USD Billion) |
MARKET SIZE 2032 | 12.5(USD Billion) |
SEGMENTS COVERED | Business Function ,Deployment Type ,Industry Vertical ,Data Source ,Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | Increasing data accessibility Growing need for location intelligence Advancements in AI and ML Rise of cloudbased services Expansion of IoT |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | Oracle Maps ,SAP ,Venntive ,Microsoft Azure Maps ,Mapbox ,Carto ,Uber ,IBM ,Esri ,Quantum GIS ,Google Maps Platform ,Here Technologies ,Pitney Bowes ,TomTom ,Precisely |
MARKET FORECAST PERIOD | 2024 - 2032 |
KEY MARKET OPPORTUNITIES | AIdriven insights and location intelligence Cloudbased solutions for scalability and flexibility Realtime data analytics and visualization Predictive analytics for proactive decisionmaking Industryspecific solutions with tailored functionality |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 14.71% (2024 - 2032) |
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
OMOP2OBO Measurement Mappings V1.0
The mappings in this repository were created between OMOP standard measurement concepts (i.e., LOINC) to the Human Phenotype Ontology (HPO), Chemical Entities of Biological Interest (CheBI), Vaccine Ontology (VO), National Center for Biotechnology Information Taxon Ontology (NCBITaxon), Protein Ontology (PRO), Cell Ontology (CL), and the Uber-anatomy Ontology (UBERON).
For each measurement, all levels of the test result (results above, below, and within a reference range) were mapped, not only those deemed clinically relevant. Results outside of a reference range, but not currently deemed clinically relevant (as advised by the literature or consultation via domain expert), were annotated to the nearest relevant ontology concept ancestor. For example, when annotating the results of a test for Asparagus IgE Ab RAST class [Presence] in Serum (LOINC:15547-3), a result above a reference range would be annotated with an increased anti-plant-based food allergen IgE antibody level (HP:0410228). While a low level of this antibody may not be deemed clinically relevant, it is still outside of the provided reference range and thus was annotated to the nearest applicable concept ancestor, abnormal immunoglobulin level (HP:0010701). There is one exception to this rule: all measured drugs and toxins (entities not normally found in the human body) with normal results (results that were not outside of a given reference range) were annotated to the same HP concept as the clinically relevant result and logically negated. For example, Amphetamine [Presence] in Urine by Screen (LOINC:19343-3), a positive finding was mapped to a positive urine amphetamine test (HP:0500112) and a negative finding was mapped to a positive urine amphetamine test and logically negated (NOT HP:0500112).
LOINC2HPO currently aligns LOINC to HP. The current work extends existing LOINC2HPO annotations to match the OMOP2OBO mappings in the following two ways: (1) annotations were updated if new and/or more specific concepts had been added to the HP; and (2) existing mappings were expanded to include the measurement substance (body fluids, tissues, and organs via Uberon), the entity being measured (chemicals, metabolites, or hormones via ChEBI; cell types via CL; and proteins and protein complexes via PR), and the species of the measured entities (organism taxonomy via NCBITaxon). Consistent with LOINC2HPO, all measurements lacking sufficient specimen detail (those measured in non-specific body substances) were annotated as “Unspecified Sample” and all measurements without a valid result type were annotated as “Not Mapped test Type”. All modifications to the original LOINC2HPO annotations were meticulously recorded in the mapping evidence field enabling users to easily identify when an original LOINC2HPO annotation had been updated.
For this OMOP domain, the owl:complementOf (“not” and was used to model normal test results), owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.
Mapping Details
Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.
Mapping Categories
Mapping Statistics
Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:
Mapping Category | HPO | UBERON | ChEBI | CL | PR | NCBITaxon |
---|---|---|---|---|---|---|
Automatic One-to-One Concept | 20 | 1981 | 268 | 129 | 19 | 286 |
Automatic One-to-Many Concept | 49 | 5 | 0 | 24 | 0 | 0 |
Automatic One-to-One Ancestor | 43 | 426 | 1149 | 5 | 5 | 207 |
Automatic Constructor - Ancestor | 0 | 1 | 12 | 1 | 0 | 0 |
Cosine Similarity | 113 | 50 | 160 | 35 | 45 | 56 |
Manual | 10663 | 319 | 1446 | 185 | 1590 | 2357 |
Manual One-to-Many | 49 | 1118 | 528 | 18 | 133 | 196 |
UnMapped | 184 | 184 | 529 | 3688 | 2296 | 982 |
Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.
Caveats: Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.
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