动态基因调控网演化分析

动态基因调控网是展现生物体内基因与基因之间相互关系随时间变化而变化的动力学行为的复杂网络.这种相互作用关系可以分为两类：激励和抑制.对动态基因调控网网络演化的研究，可以预测未来时刻生物体内的基因调控关系，从而在疾病预测和诊断、药物开发、生物学实验等领域起到重要的指导和辅助作用.现实世界中，动态基因调控网的网络演化是一个复杂而巨大的系统，当前，对于其演化机制的研究存在只关注静态网络而忽略动态网络和只关注相互作用关系而忽略相互作用类型的缺陷.针对上述问题，提出了一种动态基因调控网演化分析方法（dynamic gene regulatory network evolution analyzing method，简称DGNE），将研究扩展到了动态带符号网络领域.通过该方法包含的基于模体转换概率的连边预测算法（link prediction algorithm based on motif transfer probability，简称MT）和基于隐空间特征的符号判别算法，能够动态地捕捉基因调控网的演化机制，并准确地预测未来时刻基因调控网的连边情况.实验结果表明，DGNE方法在仿真数据集和真实数据集上均有良好的表现.

Dynamic Gene Regulatory Network Evolution Analysis

Dynamic gene regulatory network is a complex network representing the dynamic interactions between genes in organism. The interactions can be divided into two groups, motivation and inhibition. The researches on the evolution of dynamic gene regulatory network can be used to predict the gene regulation relationship in the future, thus playing a reference role in diagnosis and prediction of diseases, Pharma projects, and biological experiments. However, the evolution of gene regulatory network is a huge and complex system in real world, the researches about its evolutionary mechanism only focus on statics networks but ignore dynamic networks as well as ignore the types of interaction. In response to these defects, a dynamic gene regulatory network evolution analyzing method (DGNE) is proposed to extend the research to the field of dynamic signed networks. According to the link prediction algorithm based on motif transfer probability (MT) and symbol discrimination algorithm based on latent space character included in DGNE, the evolution mechanism of dynamic gene regulatory network can be dynamically captured as well as the links of gene regulatory network are predicted precisely. The experiment results showed that the proposed DGNE method performs greatly on simulated datasets and real datasets.