Semantic similarity in biomedical ontologies

Abstract

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.

Figure 1. Section of the GO graph showing the three aspects (molecular function, biological process, and cellular component) and some of their descendant terms.

The fact that GO is a DAG rather than a tree is illustrated by the term “transcription factor activity” which has two parents. An example of a part of relationship is also shown between the terms cell part and cell.

Figure 2. Main approaches for comparing terms: node-based and edge-based and the techniques used by each approach.

DCA, disjoint common ancestors; IC, information content; MICA, most informative common ancestor.

Figure 3. Main approaches for comparing gene products: pairwise and groupwise and the techniques used by each approach.