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Maximizing the information content of experiments in systems biologyIn silico model-based inference: a contemporary approach for hypothesis testing in network biologyParameter trajectory analysis to identify treatment effects of pharmacological interventionsA framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.Optimal experiment selection for parameter estimation in biological differential equation models.Optimal experiment design for model selection in biochemical networks.Model selection in systems biology depends on experimental design.A Bayesian active learning strategy for sequential experimental design in systems biology.Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.Clustering reveals limits of parameter identifiability in multi-parameter models of biochemical dynamics.Iterative experiment design guides the characterization of a light-inducible gene expression circuitThe Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.Computational models of the JAK1/2-STAT1 signalingModel-based design of experiments for cellular processes.A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Parameter estimation for dynamical systems with discrete events and logical operations.Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resamplingPrediction uncertainty and optimal experimental design for learning dynamical systems.How to deal with parameters for whole-cell modelling.Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.Trajectory-oriented Bayesian experiment design versus Fisher A-optimal design: an in depth comparison study.Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters.PEITH(Θ) - Perfecting Experiments with Information Theory in Python with GPU support.Optimal Quantification of Contact Inhibition in Cell Populations.A Poisson-Fault Model for Testing Power Transformers in Service
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P2860
description
2012 nî lūn-bûn
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2012年の論文
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2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
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2012年论文
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2012年论文
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name
A Bayesian approach to targeted experiment design.
@ast
A Bayesian approach to targeted experiment design.
@en
type
label
A Bayesian approach to targeted experiment design.
@ast
A Bayesian approach to targeted experiment design.
@en
prefLabel
A Bayesian approach to targeted experiment design.
@ast
A Bayesian approach to targeted experiment design.
@en
P2093
P2860
P356
P1433
P1476
A Bayesian approach to targeted experiment design
@en
P2093
C A Tiemann
P A J Hilbers
P2860
P304
P356
10.1093/BIOINFORMATICS/BTS092
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P577
2012-02-24T00:00:00Z