TY - GEN
T1 - Modeling and identification of gene regulatory networks
T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
AU - Zhang, Z. G.
AU - Hung, Y. S.
AU - Chan, S. C.
AU - Xu, W. C.
AU - Hu, Y.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Gene regulatory network
KW - Granger causality
KW - Regularization
KW - Time-series genomic data
KW - Variable selection
KW - Vector autoregressive model
UR - https://www.scopus.com/pages/publications/78149347760
U2 - 10.1109/ICMLC.2010.5580719
DO - 10.1109/ICMLC.2010.5580719
M3 - 会议稿件
AN - SCOPUS:78149347760
SN - 9781424465262
T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
SP - 3073
EP - 3078
BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Y2 - 11 July 2010 through 14 July 2010
ER -