Guo Shun, Jiang Qingshan, Chen Lifei, Guo Donghui
Department of Electronic Engineering, Xiamen University, Fujian, 361005, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000, China.
BMC Bioinformatics. 2016 Dec 28;17(1):545. doi: 10.1186/s12859-016-1398-6.
Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.
We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods.
Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.
从微阵列基因表达数据推断基因调控网络（GRN）的拓扑结构有许多潜在应用，如识别候选药物靶点并深入了解生物过程。由于数据存在噪声且维度高，以及存在大量潜在相互作用，这仍然是一个挑战。
我们引入了一种集成基因调控网络推断方法PLSNET，该方法将具有p个基因的GRN推断问题分解为p个子问题，并使用基于偏最小二乘法（PLS）的特征选择算法解决每个子问题。然后，我们的方法使用一种统计技术来细化预测。该方法在DREAM4和DREAM5基准数据集上进行了评估，比那些竞赛的获胜者和其他现有最先进的GRN推断方法具有更高的准确性。
在包括计算机模拟和体内网络在内的不同基准数据集上实现的卓越准确性表明，PLSNET达到了现有最先进的性能。
