刘飞.约束调控结点的基因网络构建算法[J].电子测量技术,2017,40(6):89-92
约束调控结点的基因网络构建算法
Inferring gene networks with constrained regulatory nodes
  
DOI:
中文关键词:  贝叶斯网络  网络构建  基因调控网络  计算复杂度
英文关键词:Bayesian network  network construction  gene regulatory networks  computational complexity
基金项目:宝鸡市科技计划(15RKX 1 5 18)、宝鸡文理学院科研(ZK16016,ZK16032)项目资助
作者单位
刘飞 宝鸡文理学院物理与光电技术学院宝鸡721016 
AuthorInstitution
Liu Fei Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Science, Baoji 721016, China 
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中文摘要:
      从实验数据构建基因调控网络是计算生物学领域的一个研究热点,但是启发式搜索、基因最大父结点数量限制策略和条件最优搜索等构建方法的计算复杂度都较大。启发式搜索方法的缺陷众所周知,在启发式搜索策略中很少有人限制基因结点的父结点数量,且搜索结果为了达到最优使得算法的时间复杂度变得很高。通过理论分析和实验结果,说明了最大父结点数量选取问题的优点和缺点,然后利用最优搜索方法融合最大父结点数量选取优点和已知基因调控网络拓扑信息,提出了新的基因调控网络构建方法。该方法利用贝叶斯网络框架实现,并在不同规模和拓扑结构的生物分子数据,真实网络数据和计算机人工合成数据集上进行测试,实验结果显示,该方法比现存的最优搜索算法有更快的计算速度。
英文摘要:
      Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum number of parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose a novel algorithm. This algorithm is implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. It can overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.
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