This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)
Exploring biological interaction networks with tailored weighted quasi-bicliques
1 Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
2 Department of Computer Science, Free University of Berlin, 14195 Berlin, Germany
3 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
4 Current address: Luxembourg Centre for Systems Biology, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
BMC Bioinformatics 2012, 13(Suppl 10):S16 doi:10.1186/1471-2105-13-S10-S16Published: 25 June 2012
Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges.
We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks.
We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks.