文章摘要
潘蕾,秦攀,顾宏.基于蚁群算法的肿瘤驱动通路搜索方法研究[J].,2018,58(2):180-186
基于蚁群算法的肿瘤驱动通路搜索方法研究
Research on searching method of driver pathways in tumor based on ant colony algorithm
  
DOI:10.7511/dllgxb201802011
中文关键词: 细胞信号通路  覆盖性  排他性  突变频率  蚁群算法
英文关键词: cell signal pathway  coverage  exclusivity  mutation frequency  ant colony algorithm
基金项目:国家自然科学基金资助项目(6163300661502074);中国博士后科学基金资助项目(2016M591430);大连理工大学基本科研业务费专项资金资助项目(DUT17RC(4)09).
作者单位
潘蕾,秦攀,顾宏  
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中文摘要:
      肿瘤的发生和发展主要是由基因突变的累积导致细胞信号通路调控紊乱而引起的.将通路中基因集的两个特性——高覆盖性和高排他性,与基因协变量相结合,提出了一种基于蚁群优化算法的驱动通路搜索方法.旨在基因突变数据及基因协变量数据的基础上,搜索满足高覆盖性和高排他性的通路基因集,从而识别致癌的驱动通路.首先,通过对基因表达水平、复制时间和染色体状态3个基因协变量相互的关联性,以及它们与基因突变频率的相关性进行分析,选择复制时间作为影响基因突变频率的权重协变量.然后,将权重协变量与现有方法结合,构造了一个新的最大权重子矩阵函数作为组合优化问题的目标函数.为克服该优化问题的NP难题,采用蚁群优化算法求解.应用该方法在肺腺癌的突变数据上进行了分析与验证.结果证明,该方法不仅比现有方法找到了更多在已证实通路中的癌基因,而且其中包含多个互斥性显著的基因对,证明了方法的有效性.
英文摘要:
      The occurrence and development of tumor are mainly caused by the accumulation of gene mutations, which leads to the disorder of cell signal pathways. There are two properties of gene sets in a pathway, i.e., high coverage and high exclusivity. A driver pathway searching method is proposed based on ant colony optimization algorithm by combining gene covariates with these two properties. In this way, cancer-causing driver pathways are identified by searching for highly covered and highly exclusive gene sets in a pathway based on the gene mutation data and covariate data. First, by analyzing the correlations between the three gene covariates, i.e., gene expression level, replication time and hic compartment, and their correlations with the gene mutation frequency, replication time is selected as the weight covariate of the gene mutation frequency. Then, a novel maximum weight submatrix function is constructed by combining the weight covariate with existing methods as the objective function of the combinatorial optimization problem. Finally, the ant colony optimization algorithm is introduced to overcome the NP problem of this optimization problem. The proposed method is applied to the lung adenocarcinoma mutation data and the results show that compared with the existing methods the proposed method can identify more cancer genes, some of which are involved in known pathways. In addition, the detected gene pairs with significant exclusivity are contained, all of these prove the efficiency of the proposed method.
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