CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data. Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.
References in zbMATH (referenced in 1 article , 1 standard article )
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- Penfold, Christopher A.; Shifaz, Ahmed; Brown, Paul E.; Nicholson, Ann; Wild, David L.: CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data (2015)