title: Functional clustering of time series gene expression data by Granger causality.
abstract: BACKGROUND
  A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.
  
  
  RESULTS
  In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence.
  
  
  CONCLUSIONS
  This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.
authors.affiliation: Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010, São Paulo 05508-090, Brazil. fujita@ime.usp.br
authors.forename: André
authors.initials: A
authors.lastname: Fujita
authors.forename: Patricia
authors.initials: P
authors.lastname: Severino
authors.forename: Kaname
authors.initials: K
authors.lastname: Kojima
authors.forename: João Ricardo
authors.initials: JR
authors.lastname: Sato
authors.forename: Alexandre Galvão
authors.initials: AG
authors.lastname: Patriota
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006367:HeLa Cells; D006801:Humans; D013997:Time Factors