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Optimizing Experimental Design in Genetics

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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.

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Acknowledgements

We thank Jason LaCombe for helping edit this manuscript. We thank Prof. Robert Strawderman for advice on model misspecification, which resulted in the proof of Proposition 3.2.

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  1. Nature Source Genetics, 33 Thornwood Drive, Suite 300, Ithaca, NY, 14850, USA

    B. McClosky & S. D. Tanksley

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  1. B. McClosky
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  2. S. D. Tanksley
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Correspondence to B. McClosky.

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Communicated by Alberto D’Onofrio.

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McClosky, B., Tanksley, S.D. Optimizing Experimental Design in Genetics. J Optim Theory Appl 157, 520–532 (2013). https://doi.org/10.1007/s10957-012-0172-9

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  • DOI: https://doi.org/10.1007/s10957-012-0172-9

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