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Granger Causality in Systems Biology: Modeling Gene Networks in Time Series Microarray Data Using Vector Autoregressive Models

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 6268)

Abstract

Understanding the molecular biological processes underlying disease onset requires a detailed description of which genes are expressed at which time points and how their products interact in so-called cellular networks. High-throughput technologies, such as gene expression analysis using DNA microarrays, have been extensively used with this purpose. As a consequence, mathematical methods aiming to infer the structure of gene networks have been proposed in the last few years. Granger causality-based models are among them, presenting well established mathematical interpretations to directionality at the edges of the regulatory network. Here, we describe the concept of Granger causality and explore recent advances and applications in gene expression regulatory networks by using extensions of Vector Autoregressive models.

Keywords

  • Granger causality
  • vector autoregressive model
  • regulatory network
  • time series
  • gene expression data

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Fujita, A., Severino, P., Sato, J.R., Miyano, S. (2010). Granger Causality in Systems Biology: Modeling Gene Networks in Time Series Microarray Data Using Vector Autoregressive Models. In: Ferreira, C.E., Miyano, S., Stadler, P.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2010. Lecture Notes in Computer Science(), vol 6268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15060-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-15060-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15059-3

  • Online ISBN: 978-3-642-15060-9

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