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Novel Biclustering Methods for Re-ordering Data Matrices

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Optimization and Data Analysis in Biomedical Informatics

Part of the book series: Fields Institute Communications ((FIC,volume 63))

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Abstract

Clustering of large-scale data sets is an important technique that is used for analysis in a variety of fields. However, a number of these methods are based on heuristics for the identification of the best arrangement of data points. In this chapter, we present rigorous clustering methods based on the iterative optimal re-ordering of data matrices. Distinct Mixed-integer linear programming (MILP) models have been implemented to carry out clustering of dense data matrices (such as gene expression data) and sparse data matrices (such as drug discovery and toxicology). We present the capability of the optimal re-ordering methods on a wide array of data sets from systems biology, molecular discovery and toxicology.

Mathematics Subject Classification (2010): Primary 54C40, 14E20, Secondary 46E25, 20C20

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Acknowledgements

CAF gratefully acknowledges financial support from the National Science Foundation, National Institutes of Health (R01 GM52032; R24 GM069736) and U.S. Environmental Protection Agency EPA (GAD R 832721-010).

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Authors and Affiliations

  1. Department of Molecular Biology, Princeton University, Princeton, NJ, 08540, USA

    Peter A. DiMaggio Jr.

  2. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08540, USA

    Ashwin Subramani & Christodoulos A. Floudas

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  1. Peter A. DiMaggio Jr.
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  2. Ashwin Subramani
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  3. Christodoulos A. Floudas
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Correspondence to Christodoulos A. Floudas .

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  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

  2. , Department of Mathematics, University of Waterloo, University Avenue West 200, Waterloo, N2L 3G1, Ontario, Canada

    Thomas F. Coleman

  3. , Department of Industrial Engineering, University of Central Florida, Central Florida Blvd 4000, Orlando, 32816, Florida, USA

    Petros Xanthopoulos

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DiMaggio, P.A., Subramani, A., Floudas, C.A. (2013). Novel Biclustering Methods for Re-ordering Data Matrices. In: Pardalos, P., Coleman, T., Xanthopoulos, P. (eds) Optimization and Data Analysis in Biomedical Informatics. Fields Institute Communications, vol 63. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4133-5_1

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