Semantic Similarity in Biomedical Ontologies

By Catia Pesquita, Daniel Faria, André O. Falcão, Phillip W. Lord, Francisco M. Couto. PLoS Comput. Biol., 2009

831 citations recorded by the source corpus.

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/079b4a023b07df979f20d543df60004dcca0630c

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Citations in the corpus

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms (2011, methodology): “The complex structure of the GO warrants a solution that takes into account the terms’ proximity in the GO graph, quantified by the GO term ‘semantic similarity’ measures [8].”
Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation (2009, methodology): “Many existing algorithms have been developed and tested using the GO to measure the semantic similarity of annotations and provide a good starting point for analysis (for example, see [18–21]).”
Analysis of chemical and biological features yields mechanistic insights into drug side effects. (2013, methodology): “For features organized in a DAG, we used the set-set Lin’s semantic similarity (Pesquita et al., 2009), where the proportion of drugs annotated to each of the overrepresented features was used to derive the information content required by Lin’s measure.”
Joint Video and Text Parsing for Understanding Events and Answering Queries (2013, methodology): “Several approaches, e.g., [59], [60], [61], can be used to measure the semantic distance given the taxonomy in an ontology.”
Information-theoretic evaluation of predicted ontological annotations (2013, background): “More elaborate distinctions are provided by Guzzi et al. (2012) and Pesquita et al. (2009).”
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, methodology): “connect genes annotated with the same GO term (Pesquita et al., 2009).”
A Census of Human Soluble Protein Complexes (2012, neutral): “…by pruning loosely connected interactions using a computational diffusion procedure calibrated by protein colocalization semantic similarity scores (Pesquita et al., 2009; Yang et al., 2012) to enforce local network topologies more consistent with annotated complexes from the withheld portion of…”