Combining Expression Data and Knowledge Ontology for Gene Clustering and Network Reconstruction

Abstract

Many methods have been developed for reverse engineering gene networks from time series expression data. However, when the number of genes and the complexity of regulation increase, it becomes increasingly difficult to infer gene networks. To tackle this scalability problem, this study presents an approach with two phases: gene clustering and network reconstruction. To perform gene clustering, a hybrid method of data and knowledge-based clustering was developed to calculate both data and semantic similarity between genes. In the network reconstruction procedure, a Boolean network model that was inferred from the gene clusters was used to represent the network. A series of experiments were conducted to investigate the effect of the hybrid similarity measure in gene clustering and network reconstruction. The results prove the feasibility and effectiveness of the proposed approach.