IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0235670.html
   My bibliography  Save this article

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

Author

Listed:
  • Duc-Hau Le

Abstract

Background: Biomedical 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. Results: In 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. Conclusions: Taken 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

Suggested Citation

  • Duc-Hau Le, 2020. "UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-18, July.
  • Handle: RePEc:plo:pone00:0235670
    DOI: 10.1371/journal.pone.0235670
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235670
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0235670&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0235670?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
    2. Gaston K Mazandu & Nicola J Mulder, 2014. "Information Content-Based Gene Ontology Functional Similarity Measures: Which One to Use for a Given Biological Data Type?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-20, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles Bettembourg & Christian Diot & Olivier Dameron, 2015. "Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
    2. Karamollah Bagherifard & Mohsen Rahmani & Vahid Rafe & Mehrbakhsh Nilashi, 2018. "A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-26, March.
    3. Kanchan Jha & Sriparna Saha & Pratik Dutta, 2024. "Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach," Annals of Operations Research, Springer, vol. 339(3), pages 1793-1811, August.
    4. Dongmin Bang & Sangsoo Lim & Sangseon Lee & Sun Kim, 2023. "Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. Peng Wang & Shangwei Ning & Qianghu Wang & Ronghong Li & Jingrun Ye & Zuxianglan Zhao & Yan Li & Teng Huang & Xia Li, 2013. "mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    6. Tom Narock & Lina Zhou & Victoria Yoon, 2013. "Semantic similarity of ontology instances using polarity mining," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 416-427, February.
    7. Xiaomei Wu & Erli Pang & Kui Lin & Zhen-Ming Pei, 2013. "Improving the Measurement of Semantic Similarity between Gene Ontology Terms and Gene Products: Insights from an Edge- and IC-Based Hybrid Method," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    8. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.
    9. Yi-An Chen & Lokesh P Tripathi & Benoit H Dessailly & Johan Nyström-Persson & Shandar Ahmad & Kenji Mizuguchi, 2014. "Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    10. Fran Supek & Matko Bošnjak & Nives Škunca & Tomislav Šmuc, 2011. "REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-9, July.
    11. Charles Bettembourg & Christian Diot & Olivier Dameron, 2014. "Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    12. Adrian M Altenhoff & Romain A Studer & Marc Robinson-Rechavi & Christophe Dessimoz, 2012. "Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-10, May.
    13. Tiago Grego & Francisco M Couto, 2013. "Enhancement of Chemical Entity Identification in Text Using Semantic Similarity Validation," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-9, May.
    14. Nurul Aswa Omar & Shahreen Kasim & Mohd. Farhan Md. Fuzzee & Azizul Azhar Ramli & Hairulnizam Mahdin & Seah Choon Sen, 2017. "A Review on Feature based Approach in Semantic Similarity for Multiple Ontology," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 1(1), pages 7-9, February.
    15. Laia Subirats & Luigi Ceccaroni & Felip Miralles, 2012. "Knowledge Representation for Prognosis of Health Status in Rehabilitation," Future Internet, MDPI, vol. 4(3), pages 1-14, August.
    16. Augusto Anguita-Ruiz & Alberto Segura-Delgado & Rafael Alcalá & Concepción M Aguilera & Jesús Alcalá-Fdez, 2020. "eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-34, April.
    17. Ilya Plyusnin & Liisa Holm & Petri Törönen, 2019. "Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-27, November.
    18. Shibiao Wan & Man-Wai Mak & Sun-Yuan Kung, 2014. "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
    19. Pisanu Buphamalai & Tomislav Kokotovic & Vanja Nagy & Jörg Menche, 2021. "Network analysis reveals rare disease signatures across multiple levels of biological organization," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    20. Gaston K Mazandu & Nicola J Mulder, 2014. "Information Content-Based Gene Ontology Functional Similarity Measures: Which One to Use for a Given Biological Data Type?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-20, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0235670. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.