构建时延基因调控网络的多数据源融合算法

A Multi-source Fusion Algorithm for Constructing Time-lagged Gene Regulation Network

为了进一步提高基因调控网络构建的精确度,提出一种基于多数据源融合的时延基因调控网络构建算法.该算法基于递归模糊神经网络模型,使用时序互信息估计基因间的转录时延,并限制每个基因的潜在调控基因,从而有效提高建网的效率.在网络结构学习阶段,使用离散多目标粒子群优化(discrete multi-objective particle swarm optimization,dMOPSO)算法实现从时序基因表达数据和CHIP-chip数据共同构建基因调控网络.人工模拟数据和酵母菌细胞周期表达数据的实验结果表明该算法能正确选出潜在的调控基因,从而更加精确地构建基因调控网络.

In order to further improve the accuracy of the constructed gene regulation network, this paper puts forward an algorithm to construct time-lagged gene regulation network based on multi-source fusion. Based on recurrent fuzzy neural network model, the algo- rithm estimates transcription delay between genes using time series mutual information and also limits potential regulation genes of each gene, thus effectively improves the efficiency of the constructed network. In the phase of structure learning, the algorithm commonly constructs gene regulation network from time series gene expression data and CHIP-chip data using discrete multi-objective particle swarm optimization （dMOPSO） algorithm. Experimental results on artificial simulation data and yeast cell cycle expression data show that the proposed algorithm can correctly choose potential regulation genes, and thereby more accurately constructs gene regulation network.