We report on a new approach to modelling and identifying dependencies within a gene regulatory cycle. In particular, we aim to learn the structure of a causal network from gene expression microarray data. We model causality in two ways: by using conditional dependence assumptions to model the independence of different causes on a common effect; and by relying on time delays between cause and effect. Networks therefore incorporate both probabilistic and temporal aspects of regulation. We are thus able to deal with cyclic dependencies amongst genes, which is not possible in standard Bayesian networks. However, our model is kept deliberately simple to make it amenable for learning from microarray data, which typically contains a small number of samples for a large number of genes. We have developed a learning algorithm for this model which was implemented and experimentally validated against simulated data and on yeast cell cycle microarray time series data sets.
机译:使用微阵列数据构建基因因果监管网络,其具有内在依赖系数
机译:从基因组数据中的多元信息中学习具有潜在变量的因果网络
机译:从系统生物学时间过程数据中学习因果网络:矢量自回归过程的有效模型选择过程
机译:从微阵列数据中学习因果网络
机译:使用机器学习和神经网络进行微阵列基因表达数据分析。
机译:利用具有内在依赖性系数的微阵列数据构建基因因果调控网络
机译:使用微阵列数据构建基因因果监管网络,其具有内在依赖系数