Zhou Chen, Zhang Shao-Wu, Liu Fei
Int J Data Min Bioinform. 2015;12(3):328-42. doi: 10.1504/ijdmb.2015.069658.
During the past decades, numerous computational approaches have been introduced for inferring the GRNs. PCA-CMI approach achieves the highest precision on the benchmark GRN datasets; however, it does not recover the meaningful edges that may have been deleted in an earlier iterative process. To recover this disadvantage and enhance the precision and robustness of GRNs inferred, we present an ensemble method, named as JRAMF, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. The jackknife resampling procedure were first employed to form a series of sub-datasets of gene expression data, then the PCA-CMI was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with PCA-CMI algorithm, the results show that JRAMF outperforms significantly PCA-CMI method, which has a high and robust performance.
在过去几十年中，已经引入了许多计算方法来推断基因调控网络（GRNs）。主成分分析-条件互信息（PCA-CMI）方法在基准GRN数据集上实现了最高精度；然而，它无法恢复可能在早期迭代过程中被删除的有意义的边。为了弥补这一缺点并提高推断的GRNs的精度和鲁棒性，我们在这项工作中提出了一种名为JRAMF的集成方法，通过采用重采样和算术平均融合两种策略从基因表达数据中推断GRNs。首先采用留一法重采样程序形成一系列基因表达数据的子数据集，然后使用PCA-CMI从子数据集中生成相应的子网络，并通过算术平均融合策略整合这些子网络来推断最终的GRN。与PCA-CMI算法相比，结果表明JRAMF明显优于PCA-CMI方法，具有较高且稳健的性能。
