Bicnet: Efficient biclustering of biological networks to unravel non-trivial modules

Abstract

The discovery of dense biclusters in biological networks received an increasing attention in recent years. However, despite the importance of understanding the cell behavior, dense biclusters can only identify modules where genes, proteins or metabolites are strongly con­nected. These modules are thus often associated with trivial, already known interactions or background processes not necessarily related with the studied conditions. Furthermore, despite the availability of bicluster-ing algorithms able to discover modules with more flexible coherency, their application over large-scale biological networks is hampered by efficiency bottlenecks. In this work, we propose BicNET (Biclustering NETworks), an algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. First, we motivate the relevance of discovering network modules given by constant, symmetric and plaid biclustering models. Second, we propose a solution to discover these flexible modules without time and memory bottlenecks by seizing high efficiency gains from the inherent structural sparsity of networks. Results from the analysis of protein and gene interaction networks support the relevance and efficiency of BicNET.