Might be applicable to find overlapping modules in coexpression networks (given that it works with a weighted graph). http://www.biomedcentral.com/1752-0509/2/93/abstract Research article An in silico method for detecting overlapping functional modules from composite biological networks *Ioannis A Maraziotis* email <http://www.biomedcentral.com/logon/logon.asp?msg=ce>, *Konstantina Dimitrakopoulou* email <http://www.biomedcentral.com/logon/logon.asp?msg=ce> and *Anastasios Bezerianos* email <http://www.biomedcentral.com/logon/logon.asp?msg=ce> /BMC Systems Biology/ 2008, *2**:*93doi:10.1186/1752-0509-2-93 Published: 1 November 2008 Abstract (provisional) Background The ever-increasing flow of gene expression and protein-protein interaction (PPI) data has assisted in understanding the dynamics of the cell. The detection of functional modules is the first step in deciphering the apparent modularity of biological networks. However most network-partitioning algorithms consider only the topological aspects and ignore the underlying functional relationships. Results In the current study we integrate proteomics and microarray data of yeast, in the form of a weighted PPI graph. We partition the enriched PPI network with the novel DetMod algorithm and we identify 335 modules. One of the main advantages of DetMod is that it manages to capture the inter-module cross-talk by allowing a controlled degree of overlap among the detected modules. The obtained modules are densely connected in terms of protein interactions, while their members share up to a high degree similar biological process GO terms. Moreover, known protein complexes are largely incorporated in the assessed modules. Finally, we display the prevalence of our method against modules resulting from other computational approaches. Conclusions The successful integration of heterogeneous data and the concept of the proposed algorithm provide confident functional modules. We also proved that our approach is superior to methods restricted to PPI data only.