Using semantic similarity to detect features in yeast protein complexes

Biological data stored in databases can be associated with information (knowledge) such as experiments, properties and functions, response to drugs etc. Such a knowledge is often stored in biological ontologies. Gene Ontology is one of the main resource of biological knowledge providing both a categorization of terms and a source of annotation for genes and proteins. This enables the use of ontology-based methodologies for the analysis of proteins and their functions. One methodology is based on semantic based similarity measures. Recently there is a growing interest in the use of semantic based methodologies to the analysis of protein interaction data such as the prediction of protein complexes based on semantic similarity measures. Despite this interest, there is the need for an assessment of semantic measures as well as a deep study on the impact of the chosen measure in the obtained results. This paper treats the problem of using semantic similarity measure to analyse protein complexes and to improve protein complexes prediction frameworks. Tests have been performed in yeast protein complexes. Results indicate that there exists a bias among measures as well as an higher value of semantic similarity within proteins that participate in the same complex, proving both a possible use of semantic similarity protein complexes prediction and a suggestion in the measure.