Gene Ontology Semi-supervised Clustering for Prediction of Genes Functions

Abstract

Different products of gene expression work together in a cell for each living organism to achieve different biological processes. Many proteins play different roles depending on the environment of the organism for the functioning of a cell. Usually, most conventional methods are not able to analyze the functions of the genes biologically. In this thesis, we propose a gene ontology (GO) annotation based semi-supervised clustering algorithm called GO Fuzzy relational clustering (GO-FRC). In GO-FRC, one gene is allowed to be assigned to multiple clusters, and that is biologically relevant to the behavior of gene. In the clustering process, GO-FRC utilizes the useful biological knowledge, which is available in the form of a Gene Ontology, as a prior knowledge along with the gene expression data. The prior knowledge helps to improve the coherence of the groups concerning the knowledge field. The proposed GO-FRC has been tested on the two yeast (Saccharomyces cerevisiae) expression profiles datasets (Eisen and Dream 5 yeast datasets) and has compared with other state-of-the-art clustering algorithms. Experimental results imply that GO-FRC can produce more biologically relevant clusters with the use of the small amount of GO annotations.