Optimizing Experimental Design in Genetics

B. McClosky () and S. D. Tanksley
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B. McClosky: Nature Source Genetics
S. D. Tanksley: Nature Source Genetics

Journal of Optimization Theory and Applications, 2013, vol. 157, issue 2, No 12, 520-532

Abstract: 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.

Keywords: Mixed-integer programming; Experimental design; Genomics; Model misspecification (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10957-012-0172-9

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