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Optimization in Sanger sequencing

机译:Sanger测序中的优化

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The main objective of this paper is to solve the optimization problem that is associated with the classification of DNA samples in PCR plates for Sanger sequencing. To achieve this goal, we design an integer linear programming model. Given that the real instances involve the classification of thousands of samples and the linear model can only be solved for small instances, the paper includes a heuristic to cope with bigger problems. The heuristic algorithm is based on the simulated annealing technique. This algorithm obtains satisfactory solutions to the problem in a short amount of time. It has been tested with real data and yields improved results compared to some commercial software typically used in (clinical) laboratories. Moreover, the algorithm has already been implemented in the laboratory and is being successfully used. (C) 2019 Elsevier Ltd. All rights reserved.
机译:本文的主要目的是解决与Sanger测序的PCR平板中DNA样品分类相关的优化问题。为实现这一目标,我们设计了一个整数线性编程模型。鉴于实际情况涉及数千个样本的分类,并且线性模型只能为小型情况求解,本文包括应对更大的问题的启发式。启发式算法基于模拟退火技术。该算法在短时间内对问题的令人满意的解决方案获得了令人满意的解决方案。它已经用真实数据测试,与通常用于(临床)实验室的一些商业软件相比,产生改善的结果。此外,该算法已经在实验室中实现并正在成功使用。 (c)2019 Elsevier Ltd.保留所有权利。

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