Abstract
Biological network studies can provide fundamental insights into various biological tasks including the functional characterization of genes and their products, the characterization of DNA-protein interactions, and the identification of regulatory mechanisms. However, biological networks are confounded with unreliable interactions and are incomplete, and thus, their computational exploitation is fraught with algorithmic challenges. Here we introduce quasi-biclique problems to analyze 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 provide exact IP solutions that can compute 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 the network.
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References
Alexe, G., Alexe, S., Crama, Y., Foldes, S., Hammer, P.L., Simeone, B.: Consensus algorithms for the generation of all maximal bicliques. Discrete Appl. Math. 145(1), 11–21 (2004)
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E., Sevier, C., Ding, H., Koh, J., Toufighi, K., Mostafavi, S., et al.: The genetic landscape of a cell. Science 327(5964), 425 (2010)
Dietrich, B.: Some of my favorite integer programming applications at IBM. Annals of Operations Research 149(1), 75–80 (2007)
Ding, C., Zhang, Y., Li, T., Holbrook, S.: Biclustering Protein Complex Interactions with a Biclique Finding Algorithm. In: ICDM, pp. 178–187 (2006)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W H Freeman, New York (1979)
Goh, K., Cusick, M., Valle, D., Childs, B., Vidal, M., Barabási, A.: The human disease network. PNAS 104(21), 8685 (2007)
Gurobi Optimization Inc.: Gurobi Optimizer 3.0 (2010)
Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Heidelberg (2004)
Li, H., Li, J., Wong, L.: Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale. Bioinformatics 22(8), 989 (2006)
Liu, H., Liu, J., Wang, L.: Searching maximum quasi-bicliques from protein-protein interaction network. JBSE 1, 200–203 (2008)
Liu, X., Li, J., Wang, L.: Modeling protein interacting groups by quasi-bicliques: Complexity, algorithm, and application. IEEE TCBB 7(2), 354–364 (2010)
Peeters, R.: The maximum edge biclique problem is NP-complete. Discrete Appl. Math. 131(3), 651–654 (2003)
Sim, K., Li, J., Gopalkrishnan, V.: Mining maximal quasi-bicliques: Novel algorithm and applications in the stock market and protein networks. Analysis and Data Mining 2(4), 255–273 (2009)
Waksman, G.: Proteomics and Protein-Protein Interactions Biology, Chemistry, Bioinformatics, and Drug Design. Springer, Heidelberg (2005)
Wang, L.: Near Optimal Solutions for Maximum Quasi-bicliques. In: Thai, M.T., Sahni, S. (eds.) COCOON 2010. LNCS, vol. 6196, pp. 409–418. Springer, Heidelberg (2010)
Yan, C., Burleigh, J.G., Eulenstein, O.: Identifying optimal incomplete phylogenetic data sets from sequence databases. Mol. Phylogenet. Evol. 35(3), 528–535 (2005)
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Chang, WC., Vakati, S., Krause, R., Eulenstein, O. (2011). Mining Biological Interaction Networks Using Weighted Quasi-Bicliques. In: Chen, J., Wang, J., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2011. Lecture Notes in Computer Science(), vol 6674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21260-4_40
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DOI: https://doi.org/10.1007/978-3-642-21260-4_40
Publisher Name: Springer, Berlin, Heidelberg
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