scholarly journals Best Paper Selection

2021 ◽  
Vol 30 (01) ◽  
pp. 189-189
Keyword(s):  
Semantic Similarity ◽  
Likelihood Ratio ◽  
Enrichment Analysis ◽  
Biomedical Ontology ◽  
Biomedical Ontologies ◽  
Clinical Genomics ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Similarity Calculation

Le DH. UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235670 Robinson PN, Ravanmehr V, Jacobsen JOB, Danis D, Zhang XA, Carmody LC, Gargano MA, Thaxton CL, Core UNCB, Karlebach G, Reese J, Holtgrewe M, Kohler S, McMurry JA, Haendel MA, Smedley D. Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. https://www.cell.com/ajhg/fulltext/S0002-9297(20)30230-5 Slater LT, Gkoutos GV, Hoehndorf R. Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01336-2 Zheng F, Shi J, Yang Y, Zheng WJ, Cui L. A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System. https://pubmed.ncbi.nlm.nih.gov/32918476/

scholarly journals PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Jennifer L Wilson ◽  
Mike Wong ◽  
Nicholas Stepanov ◽  
Dragutin Petkovic ◽  
Russ Altman
Keyword(s):  
Semantic Similarity ◽  
Drug Target ◽  
Meta Analysis ◽  
Software Support ◽  
Disease Clusters ◽  
Language System ◽  
Drug Candidates ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Biological Phenotype

Abstract Objectives We sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool. Materials and Methods We generated Phenotype Clustering (PhenClust)—a novel application of semantic similarity for interpreting biological phenotype associations—using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool’s application, and developed Docker containers with stable installations of two UMLS versions. Results PhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus. Discussion Clustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support. Conclusion PhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity.

IAIMS and UMLS at Columbia-Presbyterian Medical Center

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S89-S93 ◽  
Author(s):  
James J. Cimino ◽  
Soumitra Sengupta
Keyword(s):  
Information Management ◽  
Management System ◽  
Medical Center ◽  
Information Management System ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Academic Information

The authors use an example to illustrate combining Integrated Academic Information Management System (IAIMS) components (applications) into an integral whole, to facilitate using the components simultaneously or in sequence. They examine a model for classifying IAIMS systems, proposing ways in which the Unified Medical Language System (UMLS) can be exploited in them.

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