Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
about
Perturbation biology: inferring signaling networks in cellular systemsDynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.Identifying the topology of signaling networks from partial RNAi data.DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical modelsThermodynamically consistent Bayesian analysis of closed biochemical reaction systems.Systematical detection of significant genes in microarray data by incorporating gene interaction relationship in biological systems.Global transcription regulation of RK2 plasmids: a case study in the combined use of dynamical mathematical models and statistical inference for integration of experimental data and hypothesis explorationHub-centered gene network reconstruction using automatic relevance determination.Topological sensitivity analysis for systems biologyReconstruction of cellular signal transduction networks using perturbation assays and linear programming.Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions.The Local Edge Machine: inference of dynamic models of gene regulation.The future of toxicity testing.Integrated inference and analysis of regulatory networks from multi-level measurements.Systems biology, emergence and antireductionism.Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.
P2860
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P2860
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
description
2009 nî lūn-bûn
@nan
2009 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@ast
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@en
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@nl
type
label
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@ast
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@en
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@nl
prefLabel
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@ast
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@en
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@nl
P2093
P2860
P356
P1433
P1476
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.
@en
P2093
Daniel Ritter
Gerhard Reinelt
Johanna Mazur
P2860
P2888
P356
10.1186/1471-2105-10-448
P577
2009-12-28T00:00:00Z
P5875
P6179
1026877983