Prediction and curation of missing biomedical identifier mappings with Biomappings

Hoyt, Charles Tapley; Hoyt, Amelia L.; Gyori, Benjamin M.

doi:10.1093/bioinformatics/btad130

Cited by 5 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such quality control checks are valuable for projects with many contributors by providing an objective, deterministic way of communicating issues to contributors that should be handled before a maintainer makes a review. As an example, Biomappings 16 uses CI to check that its semantic mappings use standardized CURIEs to reference the subject, predicate, object, and other metadata for each record. A similar process can be applied to code to check for code style, the completeness of documentation, and test coverage.…”

Section: Use Technical Workflows For Automationmentioning

confidence: 99%

The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources

Hoyt,

Gyori

2024

Sci Data

Self Cite

View full text Add to dashboard Cite

Curated resources that support scientific research often go out of date or become inaccessible. This can happen for several reasons including lack of continuing funding, the departure of key personnel, or changes in institutional priorities. We introduce the Open Data, Open Code, Open Infrastructure (O3) Guidelines as an actionable road map to creating and maintaining resources that are less susceptible to such external factors and can continue to be used and maintained by the community that they serve.

show abstract

Section: Use Technical Workflows For Automationmentioning

confidence: 99%

The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources

Hoyt,

Gyori

2024

Sci Data

Self Cite

View full text Add to dashboard Cite

show abstract

The text2term tool to map free-text descriptions of biomedical terms to ontologies

Gonçalves,

Payne,

Tan

et al. 2024

Database

View full text Add to dashboard Cite

There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types—such as disease names, cell types, or chemicals—that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze, or integrate with other datasets due to the upfront curation effort required to make the data usable—typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata—either in bulk or in a one-by-one fashion, e.g. to support autocompletion of biomedical entities in forms—we have developed an open-source tool called text2term that maps free-text descriptions of biomedical entities to controlled terms in ontologies. The tool is highly configurable and can be used in multiple ways that cater to different users and expertise levels—it is available on Python Package Index and can be used programmatically as any Python package; it can also be used via a command-line interface or via our hosted, graphical user interface–based web application or by deploying a local instance of our interactive application using Docker. Database URL: https://pypi.org/project/text2term

show abstract

Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms

Tan,

Gonçalves,

Yuan

et al. 2024

JAMIA Open

View full text Add to dashboard Cite

Objective Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration. Materials and Methods We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the unified medical language system (UMLS) Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset. Results and Discussion Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped. Conclusion We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS.

show abstract

Prediction and curation of missing biomedical identifier mappings with Biomappings

Cited by 5 publications

References 45 publications

The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources

The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources

The text2term tool to map free-text descriptions of biomedical terms to ontologies

Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms

Contact Info

Product

Resources

About