Functional clustering of time series gene expression data by Granger causality

André Fujita, Patricia Severino, Kaname Kojima, João R. Sato, Alexandre G. Patriota, Satoru Miyano

研究成果: Article査読

16 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号137
ジャーナルBMC Systems Biology
6
DOI
出版ステータスPublished - 2012 10 30
外部発表はい

ASJC Scopus subject areas

  • 構造生物学
  • モデリングとシミュレーション
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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