Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions.
Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions.
复制标题
DOI：
10.1049/iet-syb.2015.0017
复制
发表时间：
2015-12
影响因子：
2.3
通讯作者：
Emmanuel Sapin;E. Keedwell;T. Frayling
中科院分区：
文献类型：
--
作者：
Emmanuel Sapin;E. Keedwell;T. Frayling
在这项研究中，蚁群优化(ACO)算法被用来获得多个单核苷酸多态(SNPs)之间的近最佳相互作用。这种方法被用来发现少量的SNP，这些SNP被组合到决策树或联想表模型中。ACO算法被证明是非常稳健的，因为它被证明能够找到从统计角度对交互中考虑的各种数量的SNP的逻辑交互、决策树和联想表模型具有歧视性的结果。在这里发现的大量SNP已经在大型全基因组关联研究中被确定与文献中的II型糖尿病有关，这为结果增加了信心。
In this study, ant colony optimisation (ACO) algorithm is used to derive near-optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome-wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.