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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Semantic similarity in biomedical ontologies.

Catia Pesquita1, Daniel Faria, André O Falcão

  • 1LaSIGE, Faculty of Sciences, University of Lisboa, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt

Plos Computational Biology
|August 4, 2009
PubMed
Summary

Semantic similarity measures, using biomedical ontologies, help researchers compare biological entities. These methods are crucial for validating studies in gene analysis, molecular interactions, and disease research.

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Area of Science:

  • Biomedical Informatics
  • Bioinformatics
  • Computational Biology

Background:

  • Biomedical ontologies are increasingly used for annotating biological entities, enabling comparisons based on shared meaning.
  • Semantic similarity measures assess the relatedness of biological entities through their annotations, offering a valuable tool in research.

Purpose of the Study:

  • To review and classify semantic similarity measures applied to biomedical ontologies.
  • To discuss comparative assessment studies, existing implementations, and applications in biomedical research.
  • To guide researchers in selecting appropriate semantic similarity approaches for their studies.

Main Methods:

  • Classification of semantic similarity measures into node-based vs. edge-based and pairwise vs. groupwise strategies.
  • Review of comparative assessment studies and discussion of their implications.
  • Survey of existing implementations and examples of applications in biomedical research.

Main Results:

  • Semantic similarity has become a valuable tool for validating diverse biomedical studies, including gene clustering, expression data analysis, and disease gene prioritization.
  • The relevance of semantic similarity measures is expected to grow, potentially becoming as essential as sequence similarity in biomedical research.
  • Understanding different approaches aids researchers in choosing the most suitable semantic similarity measure for their specific studies.

Conclusions:

  • Semantic similarity measures are integral to modern biomedical research, facilitating robust data analysis and validation.
  • The evolution of biomedical ontologies enhances the utility and importance of semantic similarity.
  • This review provides a framework for researchers to leverage semantic similarity effectively in their work.