Biomedical Knowledge Extraction Using Fuzzy Differential Profiles and Semantic Ranking

Overview & Synopsis

Recently, technologies such as DNA microarrays allow to generate big scale of transcriptomic data used to the aim of exploring background of genes. The analysis and the interpretation of such data requires important databases and efficient mining methods, in order to extract specific biological functions belonging to a group of genes of an expression profile. To this aim, we propose here a new approach for mining transcriptomic data combining domain knowledge and classification methods. Firstly, we propose the definition of Fuzzy Differential Gene Expression Profiles (FG-DEP) based on fuzzy classification and a differential definition between the considered biological situations. Secondly, we will use our previously defined efficient semantic similarity measure (called IntelliGO), that is applied on Gene Ontology (GO) annotation terms, for computing semantic and functional similarities between genes of resulting FG-DEP and well known genetic markers involved in the development of cancers. After that, the similarity matrices will be used to introduce a novel Functional Spectral Representation (FSR) calculated through a semantic ranking of genes regarding their similarities with the tumoral markers. The FSR representation should help expert to interpret by a new way transcriptomic data and infer new genes having similar biological functions regarding well known diseases.