Predicting Gene Functions Using Semi-supervised Clustering Algorithms with Objective Function Optimization

Despite the complete sequencing of human genome, most of the gene functions are still unknown. Micro array techniques provides a fast and reliable means to analysis of the gene expression and the understanding of their function. In this context, clustering gene expression data is an essential step for gene function discovery, as groups of genes with similar expressions potentially having the same biological function. In this work, we analyze the use of external biological knowledge, such as the ones provided in ontologies to improve the functional grouping of gene expression measured from micro array data set. We propose here application of semi-supervised clustering algorithms that optimize an objective function for clustering functionally related genes. These algorithms demonstrated improvements on finding functionally related genes in relation to a previously proposed model based approach.