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.

Finding Cross Genome Patterns in Annotation Graphs

“…The taxonomic organization of vertices in an ontology, as well as node properties such as descendants and ascendants, have been considered to develop state-ofthe-art distance metrics that identify near neighbors, i.e., those that are proximal to each other in the taxonomy [11,14,17,20,28]. Consider the taxonomy of Figure 7(a).…”

“…In general, both metrics are able to assign values close to 0.0 to pairs of vertices separated by a small number of edges, and a value close to 1.0 to pairs of vertices separated by a large number of edges, e.g., (0, 1) and (8,1). However, consider the pairs (10, 17) and (11,12); d tax is able to distinguish that both pairs have different taxonomic properties, i.e., the ratio dtax(10, 17) dtax (11,12) is 0.6. Note that a value of 1.0 for this ratio implies that the taxonomic distances are judged to be similar.…”

“…However, d ps is not able to identify that these two pairs have different taxonomic properties. It assigns values such that the ratio dps(10, 17) dps (11,12) is 0.75, i.e., closer to 1.0. Next we report on the distribution of the pairwise distance d tax and d ps for several datasets (Table 3).…”

Bioinformatics and Computational Biology

“…Two main approaches, pairwise and groupwise (Pesquita et al, 2009) are typically used for this purpose. Pairwise approaches use semantic similarities between the GO terms annotating each protein, the semantic similarities are calculated for all possible pairs of terms between each set.…”

“…Global functional similarity scores between the actual proteins are usually obtained by averaging, summing or selecting the maximum of the pairwise similarity scores. For more on ontology-based semantic similarity check reviews by Pesquita et al (2009) and Gan et al (2013).…”