A Partial Granger Causality Approach to Explore Causal Networks Derived From Multi-parameter Data
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Grouped graphical Granger modeling methods for temporal causal modeling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
High dimensional data analysis using multivariate generalized spatial quantiles
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Pattern recognition in biological time series
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Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
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Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks. Contact:nitai@lilly.com; chatterjee@stat.umn.edu