Integration of Biological Data with Semantic Networks

In recent years, the broad utilization of high-throughput experimental techniques resulted in a vast amount of expression and interaction data, accompanied by information on metabolic, cell signaling and gene regulatory pathways accumulated in the literature and databases. Thus, one of the major goals of modern bioinformatics is to process and integrate heterogeneous biological data to provide an insight into the inner workings of a cell governed by complex interaction networks.

The paper reviews the current development of semantic network (SN) technologies and their applications to the integration of genomic and proteomic data. We also elaborate on our own work that applies a semantic network approach to modeling complex cell signaling pathways and simulating the cause-effect of molecular interactions in human macrophages.

The review is concluded with a discussion of the prospective use of semantic networks in bioinformatics practice as an efficient and general language for data integration, knowledge representation and inference.