Ontology-powered Semantic Similarity of Biological and Biomedical Entities - The Story So Far, and The Road Ahead

摘要

The widespread use of ontologies in biology and bio-medicine have led to the creation of ontology-powered data stores and have paved the way for large-scale computational analyses. Semantic similarity - the assessment of "relatedness" between biological objects such as genes, diseases, phenotypes, etc. has become a crucial tool for many applications. Over the years, a number of similarity metrics have been developed and used for applications such as identifying functionally similar proteins, comparing human disease phenotypes to model organism models for disease diagnosis, connecting evolutionary phenotypes to model organism gene phenotypes, etc. The vast variety of semantic similarity metrics and the diverse applications make it daunting for new adopters to select and apply an appropriate metric. While semantic similarity metrics abound, critical issues such as standardized performance evaluations, robustness, sensitivity, effect of parametric choices, and computational complexity of these metrics remain largely unexplored. This study presents a position on several critical issues that impact the accuracy and confidence of semantic similarity results. A comprehensive review of similarity metrics in four categories along with applications in biological and biomedical domains are also included.