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Optimizing Experimental Design in Genetics

Author

Listed:
  • B. McClosky

    (Nature Source Genetics)

  • S. D. Tanksley

    (Nature Source Genetics)

Abstract

Researchers in the life sciences (i.e., healthcare and agriculture) commonly use heuristics to process and interpret the vast amount of available DNA sequence data. The application of discrete optimization techniques, such as mixed-integer programming (MIP), remains largely unexplored and has the potential to transform the field. This paper reports on the successful use of MIP to optimize experimental design in a practical genetics application. More generally, our results illustrate the potential benefits of using MIP for subset selection problems in genetics.

Suggested Citation

  • B. McClosky & S. D. Tanksley, 2013. "Optimizing Experimental Design in Genetics," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 520-532, May.
    • Handle: RePEc:spr:joptap:v:157:y:2013:i:2:d:10.1007_s10957-012-0172-9
    DOI: 10.1007/s10957-012-0172-9
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    References listed on IDEAS

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    1. McClosky Benjamin & Ma Xiwen & Tanksley Steven D., 2011. "Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-19, May.
    2. Harvey J. Greenberg & William E. Hart & Giuseppe Lancia, 2004. "Opportunities for Combinatorial Optimization in Computational Biology," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 211-231, August.
    3. Butenko, S. & Wilhelm, W.E., 2006. "Clique-detection models in computational biochemistry and genomics," European Journal of Operational Research, Elsevier, vol. 173(1), pages 1-17, August.
    4. Vieira Jr., Hélcio & Sanchez, Susan & Kienitz, Karl Heinz & Belderrain, Mischel Carmen Neyra, 2011. "Generating and improving orthogonal designs by using mixed integer programming," European Journal of Operational Research, Elsevier, vol. 215(3), pages 629-638, December.
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