Loading [a11y]/accessibility-menu.js
Modeling and identification of gene regulatory networks: A Granger causality approach | IEEE Conference Publication | IEEE Xplore

Modeling and identification of gene regulatory networks: A Granger causality approach


Abstract:

It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large...Show More

Abstract:

It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced.
Date of Conference: 11-14 July 2010
Date Added to IEEE Xplore: 20 September 2010
ISBN Information:

ISSN Information:

Conference Location: Qingdao, China

References

References is not available for this document.