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ESANN
2003
13 years 8 months ago
Autonomous learning algorithm for fully connected recurrent networks
In this paper fully connected RTRL neural networks are studied. In order to learn dynamical behaviours of linear-processes or to predict time series, an autonomous learning algori...
Edouard Leclercq, Fabrice Druaux, Dimitri Lefebvre
BMCBI
2005
189views more  BMCBI 2005»
13 years 6 months ago
Quantitative inference of dynamic regulatory pathways via microarray data
Background: The cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway ar...
Wen-Chieh Chang, Chang-Wei Li, Bor-Sen Chen
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
BMCBI
2008
135views more  BMCBI 2008»
13 years 7 months ago
Inferring the role of transcription factors in regulatory networks
Background: Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple...
Philippe Veber, Carito Guziolowski, Michel Le Borg...
IDA
2003
Springer
14 years 6 days ago
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...
Allan Tucker, Xiaohui Liu