The hierarchical organization of biological complexity can be represented as a multi-layered chart, in which each layer represents a domain of knowledge. A wealth of data is publically available through databases on each of these domains, albeit rarely does data from one domain relates to data on another. Integration and mining of these datasets could prove a powerful approach to novel discoveries. However, these databases are often built using different architectures and standards, which hampers their straightforward integration. To tackle this problem, we created integrated Complex Traits Networks (iCTNet), a bioinformatic tool (Cytoscape plugin) that allows download, visualization and analysis of data from 5 different domains (genomics, protein-interactions, protein-DNA interactions, drug-target, gene expression) (Wang et al. BMC Bioinformatics; 2011). Current efforts to expand the scope of iCTNet to include more databases and functionality are underway. Methods: Bioinformatics, advanced statistics, graph theory.