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Optimal decoding and minimal length for the non-unique oligonucleotide probe selection problem

机译：非唯一寡核苷酸探针选择问题的最佳解码和最小长度

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摘要

One of the applications of DNA microarrays is recognizing the presence or absence of different biological components (targets) in a sample. Hence, the quality of the microarrays design which includes selecting short Oligonucleotide sequences (probes) to be affixed on the surface of the microarray becomes a major issue. A good design is the one that contains the minimum possible number of probes while having an acceptable ability in identifying the targets existing in the sample. This paper focuses on the problem of computing the minimal set of probes which is able to identify each target of a sample, referred to as non-unique oligonucleotide probe selection. We present the application of an estimation of distribution algorithm (EDA) named Bayesian optimization algorithm (BOA) to this problem, for the first time. The proposed approach considers integration of BOA and one simple heuristic introduced for the non-unique probe selection problem. The results provided by this approach compare favorably with the state-of-the-art methods in the single target case. While most of the recent research works on this problem has been focusing on the single target case only, we present the application of our method in integration with decoding approach in a multiobjective optimization framework for solving the problem in the case of multiple targets.
机译：DNA微阵列的一种应用是识别样品中是否存在不同的生物成分（靶标）。因此，包括选择短的寡核苷酸序列（探针）以附着在微阵列表面上的微阵列设计的质量成为主要问题。一个好的设计是包含尽可能少的探针，同时具有识别样品中存在的靶标的可接受能力。本文关注于计算能够识别样品的每个靶标的最小探针组的问题，称为非唯一寡核苷酸探针选择。我们首次提出了名为贝叶斯优化算法（BOA）的分布估计算法（EDA）的应用。所提出的方法考虑了BOA的集成以及针对非唯一探测器选择问题引入的一种简单启发式方法。这种方法提供的结果与单个目标案例中的最新方法相比具有优势。尽管最近有关此问题的大多数研究工作仅集中在单个目标案例上，但我们在多目标优化框架中将我们的方法与解码方法集成在一起，以解决多目标案例中的问题。

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