title: CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data.
abstract: 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.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Penfold
authors.forename: Ahmed
authors.initials: A
authors.lastname: Shifaz
authors.forename: Paul E
authors.initials: PE
authors.lastname: Brown
authors.forename: Ann
authors.initials: A
authors.lastname: Nicholson
authors.forename: David L
authors.initials: DL
authors.lastname: Wild
mesh_terms: D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D012984:Software