A Web-knowledge-based Clustering Model for Gene Expression Data Analysis

Current microarray technology provides ways to obtain time series expression data for studying a wide range of biological systems. However, the expression data tends to contain considerable noise, which as a result may deteriorate the clustering quality. We propose a webknowledge-based clustering method to incorporate the knowledge of genegene relations into the clustering procedure. Our method first obtains the biological roles of each gene through a web mining process, next groups genes based on their biological roles and the Gene Ontology, and last applies a semi-supervised clustering model where the supervision is provided by the detected gene groups. Under the guidance of the knowledge, the clustering procedure is able to cope with data noise. We evaluate our method on a publicly available data set of human fibroblast response to serum. The experimental results demonstrate improved quality of clustering compared to the clustering methods without any prior knowledge.