元分析方法在单倍型推断中的应用初探
A preliminary analysis on metaheuristics methods applied to the Haplotype Inference Problem
摘要：
单倍型推断是生物信息学中一个具有挑战性的问题，即根据二倍体生物的基因型推断其基本遗传组成。这些信息使研究人员能够对疾病中涉及的遗传变异和对治疗药物的个体反应进行关联研究。一个值得注意的方法是将其编码为组合问题（在某些假设下，如纯简约准则），并使用现成的组合优化技术来解决它。应用于单倍型推断的主要方法是简单的贪婪启发式或精确方法（半线性规划，半定规划，SAT编码），目前仅适用于中等大小的实例。我们相信，元启发式和混合方法可以提供更好的可扩展性。此外，元算法可以很容易地结合问题的具体算法，他们也可以与基于树的搜索技术，从而提供了一个很有前途的框架，混合系统中，一个很好的权衡效果和效率之间可以达到。在本文中，我们说明了一个可行性研究的方法，并讨论了一些相关的设计问题，如建模和设计的近似求解器相结合的建设性算法，局部搜索为基础的改进策略和学习机制。除了单倍型推断问题本身的相关性之外，这个初步分析也是一个有趣的案例研究，因为问题的制定在建模和混合元启发式求解器设计方面提出了一些挑战，这些挑战可以推广到其他问题。
英文摘要：
Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search techniques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modeling and design of approximate solvers that combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype Inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modeling and hybrid metaheuristic solver design that can be generalized to other problems.
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