Semantic Similarity Definition over Gene Ontology by Further Mining of the Information Content

Abstract

The similarity of two gene products can be used to solve many problems in information biology. Since one gene product corresponds to several GO (Gene Ontology) terms, one way to calculate the gene product similarity is to use the similarity of their GO terms. This GO term similarity can be defined as the semantic similarity on the GO graph. There are many kinds of similarity definitions of two GO terms, but the information of the GO graph is not used efficiently. This paper presents a new way to mine more information of the GO graph by regarding edge as information content and using the information of negation on the semantic graph. A simple experiment is conducted and, as a result, the accuracy increased by 8.3 percent in average, compared with the traditional method which uses node as information source.