Abstract
Reducing the number of needed primers in multiplex polymerase chain reaction experiments is useful, or even essential, in large scale genomic research. In this paper, we transform this multiple-use primer design problem into a set-covering problem, and propose a modified compact genetic algorithm (MCGA) approach to disclose optimal solutions. Our experimental results demonstrate that MCGA effectively reduces the primer numbers of multiplex PCR experiments among whole-genome data sets of four test species within a feasible computation time, especially when applied on complex genomes. Moreover, the performance of MCGA further exhibits better global stability of optimal solutions than conventional heuristic methods that may fall into local optimal traps.
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Keywords
- Local Search
- Solution Quality
- Multiplex Polymerase Chain Reaction
- Duchenne Muscular Dystrophy
- Competition Mechanism
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Huang, YC., Chuang, HY., Tsai, HK., Chang, CF., Kao, CY. (2004). Designing Multiple-Use Primer Set for Multiplex PCR by Using Compact GAs. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_52
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DOI: https://doi.org/10.1007/978-3-540-30217-9_52
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