Solving the Order-Preserving Submatrix Problem via Integer Programming

References

• Ahuja R. K., Magnanti T. L., Orlin J. B.Network Flows: Theory, Algorithms, and Applications (1993) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
• Balasundaram B., Butenko S., Trukhanov S. Novel approaches for analyzing biological networks. J. Combin. Optim. (2005) 10(1):23–39Crossref, Google Scholar
• Baz M., Hunsaker B., Brooks P., Gosavi A. Automated tuning of optimization software parameters. (2007) . Technical report, University of Pittsburgh Department of Industrial Engineering, PittsburghGoogle Scholar
• Ben-Dor A., Chor B., Karp R., Yakhini Z. Discovering local structure in gene expression data: The order-preserving submatrix problem. Proc. 6th Annual Internat. Conf. Comput. Biology (RECOMB '02) (2002) (ACM, New York) 49–57Crossref, Google Scholar
• Ben-Dor A., Chor B., Karp R., Yakhini Z. Discovering local structure in gene expression data: The order-preserving submatrix problem. J. Comput. Biology (2003) 10(3–4):373–384Crossref, Google Scholar
• Busygin S., Prokopyev O. A., Pardalos P. M. Feature selection for consistent biclustering via fractional 0–1 programming. J. Combin. Optim. (2005) 10(1):7–21Crossref, Google Scholar
• Busygin S., Prokopyev O. A., Pardalos P. M. Biclustering in data mining. Comput. Oper. Res. (2008) 35(9):2964–2987Crossref, Google Scholar
• Cheng Y., Church G. M. Biclustering of expression data. Proc. Eighth Internat. Conf. Intelligent Systems Molecular Biology (2000) (AAAI Press, Menlo Park, CA) 93–103Google Scholar
• Cheung L., Cheung D. W., Kao B., Yip K. Y. On mining micro-array data by order-preserving submatrix. Internat. J. Bioinformatics Res. Appl. (2007) 3(1):42–64Crossref, Google Scholar
• Downey R. G., Fellows M. R.Parameterized Complexity (1999) (Springer-Verlag, New York) Crossref, Google Scholar
• Forrester R. J., Greenberg H. J. Quadratic binary programming models in computational biology. Algorithmic Oper. Res. (2008) 3(2):110–129Google Scholar
• Fung H. K., Rao S., Floudas C. A., Prokopyev O. A., Pardalos P. M., Rendl F. Computational comparison studies of quadratic assignment like formulations for the in silico sequence selection problem in de novo protein design. J. Combin. Optim. (2005) 10(1):41–60Crossref, Google Scholar
• Garey M. R., Johnson D. S.Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (W. H. Freeman & Company, New York) Google Scholar
• Hedenfalk I., Duggan D., Chen Y., Radmacher M., Bittner M., Simon R., Meltzer P., et al. Gene-expression profiles in hereditary breast cancer. New England J. Medicine (2001) 344(8):539–548Crossref, Google Scholar
• HumanGene Expression Index (2009) . HuGE index.org. http://www.hugeindex.orgGoogle Scholar
• ILOG (2007) . CPLEX 11.0 User's Manual. ILOG CPLEX Division, Incline Village, NVGoogle Scholar
• Madeira S. C., Oliveira A. L. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. Comput. Biology Bioinformatics (2004) 1(1):24–45Crossref, Google Scholar
• Meneses C. N., Lu Z., Oliveira C. A. S., Pardalos P. M. Optimal solutions for the closest-string problem via integer programming. INFORMS J. Comput. (2004) 16(4):419–429Link, Google Scholar
• Murray A. T., Church R. L. Constructing and selecting adjacency constraints. INFOR (1996) 34(3):232–248Google Scholar
• Murray A. T., Church R. L. Facets for node packing. Eur. J. Oper. Res. (1997) 101(3):598–608Crossref, Google Scholar
• Padberg M. W. On the facial structure of set packing polyhedra. Math. Programming (1973) 5(1):199–215Crossref, Google Scholar
• Schensted C. Longest increasing and decreasing subsequences. Canadian J. Math. (1961) 13(2):179–191Crossref, Google Scholar
• Sherali H. D., Adams W. P.A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems (1998) (Kluwer Academic Publishers, Dordrecht, The Netherlands) Google Scholar
• Tan M. P., Broach J. R., Floudas C. A. A novel clustering approach and prediction of optimal number of clusters: Global optimum search with enhanced positioning. J. Global Optim. (2007) 39(3):323–346Crossref, Google Scholar
• Trapp A., Prokopyev O. A., Busygin S. Finding checkerboard patterns via fractional 0–1 programming. J. Combin. Optim. (2008) . ePub ahead of print September 18, http://www.springerlink.com/content/kt061p80467t8w11Google Scholar