title: Causality and pathway search in microarray time series experiment.
abstract: BACKGROUND
  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.
authors.affiliation: Eli Lilly and Co. nitai@lilly.com
authors.forename: Nitai D
authors.initials: ND
authors.lastname: Mukhopadhyay
authors.forename: Snigdhansu
authors.initials: S
authors.lastname: Chatterjee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors
chemical_list: D020543:Proteome