Evidence map›Paper›PMID 32645039›Full record

ArticlePloS one2020

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

Duc-Hau Le

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In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. DeepDisSNP: Predicting disease-associated SNPs by representation learning on disease and SNP linkage disequilibrium networks.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
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4 · The record

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5 · Who and what money

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1 author.

Duc-Hau LeDepartment of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam.ORCID 0000-0002-4951-5916

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology.

resultsIn this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes.

conclusionsTaken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications. AVAILABILITY: UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use.

Indexed as

Biological OntologiesSoftwareBiomarkersDiagnostic Tests, RoutineHumansSemanticsVocabulary, ControlledBiomarkers

Identifiers

PMID32645039
PMCPMC7347127

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.