BinTree Seeking: A novel approach to mine both Bi-sparse and cohesive modules in protein interaction networks Introduction The modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model for systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many approaches were proposed to capture cohesive modules, the gathering of nodes into modules so that there is a higher density of edges within modules than those across them. Recently, an interesting phenomenon that cohesively interacting modules of proteins is not a universal organizing principle has attracted much attention, which opens a new avenue for revisiting functional modules in PINs. In this paper, an unorthodox structure called Bi-sparse module is proposed in PINs. In contrast to the traditional cohesive module, the nodes in Bi-sparse module are sparsely connected internally and densely connected with other Bi-sparse or cohesive modules. And then we present a novel approach, the BinTree Seeking (BTS), for mining both Bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not necessarily cohesive but can be sparse. BTS software package Click here to request the whole software package and Figure 1 shows an intuitive flowchart of how BTS works. Figure 1. An intuitive flowchart of BTS. Supporting information Click here to download the supporting information. Reference Qing-Ju Jiao, Yan-Kai Zhang, Lu-Ning Li, and Hong-Bin Shen, BinTree Seeking: A novel approach to mine both Bi-sparse and cohesive modules in protein interaction networks, PLoS ONE, 2011, 6: e27646.