ARTIVA在时间序列基因表达数据网络构建中的应用

摘要

Objective To explore the application of auto regressive time varying models in network building of time se-ries microarray data.Methods We used actual data to carry out a preliminary discussion about the properties of auto regressive time varying models.Results Analysis results of actual data suggested that auto regressive time varying models can perform well whether the number of timepoint is large or small,and it can recognize the network’s dynamic variation rule.Conclusion Auto regressive time varying models is applicable to network building of time series microarray data.%目的 探讨时间序列基因表达数据网络构建的ARTIVA模型与方法.方法 通过实例研究ARTIVA模型构建网络的效果.结果 实例分析表明,ARTIVA模型对时间序列数据具有良好的适应性,在具有3个时间点和9个时间点两种情况下,ARTIVA模型均能准确地模拟生物网络,并且能识别网络结构的动态变化过程.结论 ARTIVA模型适用于时间序列基因表达数据网络构建,具有较高的实用价值.