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» Modeling, inference and optimization of regulatory networks ...
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SAC
2008
ACM
13 years 6 months ago
Bayesian inference for a discretely observed stochastic kinetic model
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
Richard J. Boys, Darren J. Wilkinson, Thomas B. L....
BIOCOMP
2008
13 years 8 months ago
Reverse Engineering Module Networks by PSO-RNN Hybrid Modeling
Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reas...
Yuji Zhang, Jianhua Xuan, Benildo de los Reyes, Ro...
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
13 years 12 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
BMCBI
2010
110views more  BMCBI 2010»
13 years 7 months ago
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
Background: One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using mi...
Pietro Zoppoli, Sandro Morganella, Michele Ceccare...
CIBCB
2009
IEEE
13 years 8 months ago
Steady-state analysis of genetic regulatory networks modeled by nonlinear ordinary differential equations
Although Ordinary Differential Equations (ODEs) have been used to model Genetic Regulatory Networks (GRNs) in many previous works, their steady-state behaviors are not well studied...
Haixin Wang, Lijun Qian, Edward R. Dougherty