Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual inform...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.展开更多
Correctly quantifying the direct association between variables based on observed data is a valuable topic to study.On the one hand,many traditional methods can only measure the linear direct association.On the other h...Correctly quantifying the direct association between variables based on observed data is a valuable topic to study.On the one hand,many traditional methods can only measure the linear direct association.On the other hand,certain existing measures of direct association between two variables suffer an instability problem when a parent variable has a strong influence on both variables.To solve these issues,we propose a measure,namely the independent conditional mutual information(ICMI),to quantify the direct association between two variables in a three-variable network.Additionally,we use simulation data to numerically compare the stability and reliability of the ICMI with those of other measures of direct association under different conditions.The numerical results show that ICMI performs more stably in many cases than the known measures such as unique information,conditional mutual information,and partial correlation.The statistical power results show that ICMI is more reliable for different forms of function.We further use our measure to analyze a network consisting of family finance,social security,and the residence of senior citizens.展开更多
Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on ...Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150, 10926197,61201323
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.
基金supported by the Innovation Program for Quantum Science and Technology(2021ZD0301701)the National Natural Science Foundation of China(12175104).
文摘Correctly quantifying the direct association between variables based on observed data is a valuable topic to study.On the one hand,many traditional methods can only measure the linear direct association.On the other hand,certain existing measures of direct association between two variables suffer an instability problem when a parent variable has a strong influence on both variables.To solve these issues,we propose a measure,namely the independent conditional mutual information(ICMI),to quantify the direct association between two variables in a three-variable network.Additionally,we use simulation data to numerically compare the stability and reliability of the ICMI with those of other measures of direct association under different conditions.The numerical results show that ICMI performs more stably in many cases than the known measures such as unique information,conditional mutual information,and partial correlation.The statistical power results show that ICMI is more reliable for different forms of function.We further use our measure to analyze a network consisting of family finance,social security,and the residence of senior citizens.
文摘Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.
