A hybrid semantic similarity measure for gene ontology based on offspring and path length

Gene Ontology (GO), one of the fastest growing bioinformatics projects, seeks to capture the most complete knowledge about gene related products. Over the past few years, many semantic similarity measures have been proposed based on various aspects of the GO, such as the corpus-based information content of the terms or distance between the terms. In this work, we first introduce the concept of structural information content (IC) and structural least common ancestor (LCA). We then use these concepts as part of a hybrid approach to propose a novel semantic similarity measure, known as Hybrid Structural Similarity (HSS) that depends only on the structure of the ontology and not on external annotation corpus. We have carried out extensive experimentation using the entire protein interaction database of S. cerevisiae (yeast) and compared HSS against other popular methods. The results show that in all except two cases, our method is able to outperform all methods by a significant margin. Further, our method is dynamic and responds dynamically to the term additions and updates in the ontology.