Biomedical Knowledge Extraction Using Fuzzy Differential Profiles and Semantic Ranking
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.
KeywordsGene Ontology Transcriptomic Data Functional Similarity Metachronous Metastasis Semantic Similarity Measure
Unable to display preview. Download preview PDF.
- 3.Daniel, B., et al.: The GOA database in 2009–an integrated Gene Ontology Annotation resource. Nucl. Acids Res. 37(suppl. 1), D396–D403 (2009)Google Scholar
- 4.Benabderrahmane, S., Devignes, M.-D., Smaïl-Tabbone, M., Napoli, A., Poch, O., Nguyen, N.-H., Raffelsberger, W.: Analyse de données transcriptomiques: Modélisation floue de profils dexpression différentielle et analyse fonctionnelle. In: INFORSID, pp. 413–428 (2009)Google Scholar
- 5.Martin, D., Brun, C., Remy, E., Mouren, P., Thieffry, D., Jacq, B.: GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biology 5(12) (2004)Google Scholar
- 6.Dennis, G., Sherman, B., Hosack, D., Yang, J., Gao, W., Lane, H., Lempicki, R.: David: Database for annotation, visualization, and integrated discovery. Genome Biology 4(9), R60 (2003), A previous version of this manuscript was made available before peer review at http://genomebiology.com/2003/4/5/P3
- 7.Benabderrahmane, S.: Ontology-based gene set enrichment analysis using an efficient semantic similarity measure and functional clustering. In: Proceedings of the 4th International conference on Web and Information Technologies (ICWIT 2012), Sidi Bel Abbes, Algeria, April 29-30, pp. 151–159 (2012)Google Scholar
- 9.Sidahmed, B., et al.: Ontology-based functional classification of genes: Evaluation with reference sets and overlap analysis. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 201–208 (November 2011)Google Scholar
- 12.Miyoshi, N., et al.: Atp11a is a novel predictive marker for metachronous metastasis of colorectal cancer. Oncology Reports 23(2), 505–510 (2010)Google Scholar
- 14.Hiroya, T., et al.: c met expression level in primary colon cancer a predictor of tumor invasion and lymph node metastases. Clin. Cancer Res. 9(4), 1480–1488 (2003)Google Scholar