pauwels2014: Bayesian Experimental Design for Systems Biology.

Implementation of a Bayesian active learning strategy to carry out sequential experimental design in the context of biochemical network kinetic parameter estimation. This package gathers functions and pre-computed data sets to reproduce results presented in Pauwels E. et. al published in BMC Systems Biology, 2014. Scripts are given to compute all results from scratch or to draw pictures based on pre-computed data sets.

AuthorEdouard Pauwels
Date of publication2014-08-23 08:12:04
MaintainerEdouard Pauwels <pauwelsed@gmail.com>
LicenseGPL-3
Version1.0

View on CRAN

Man pages

active_design: Simulates the active design process.

add_infinitesimal: Finite difference function

add_noise: Noise generative process for the simulations

armijo: Performs armijo line searcc

BFGS_special: An implementation of BFGS method for posterior maximization.

compute_gradient: Finite difference function

compute_gradient_coordinate: Finite difference function

compute_mean_risks: Compute an average risk as a function of credit spent

dmvnorm: Gaussian multivariate density

dream6_design: Simulates the active design process using the comparison...

estimate_risk_dream6: Expected risk estimation (comparison with litterature).

estimate_risk_out_all: Expected risk estimation.

eval_kn_log_like: Evaluates the likelihood of a parameter value

eval_log_like_knobj: Posterior function.

experiment_list1: Molecular perturbations.

exps: List of possible experiments

generate_our_knowledge: Initialize a knowledge list.

generate_sample: An implementation of the Metropolis Hasting algorithm

knobjs: Knowledge lists

log_likelihood: User defined likelihood function.

log_normalize: Normalize in log space

log_prior: User defined log prior

observables: Observable quantities of the model

pauwels2014-package: Reproduce numerical experiments

proj_grad: Least square on the positive orthant

random_design: Simulates a randim design process.

read_knobjs: Summarizes pre-computed results.

reverse_params: Transform log space parameters back to the original space

risk_theta_fun: Risk function

risk_theta_vect: Expected risk based on a posterior sample

sample_function: Generates posterior samples

sample_function_multi_mod_weight: Sample function visiting multiple modes of the posterior

sample_function_single_mod: Sample function visiting a single mode of the posterior.

simulate_experiment: Simulates the dynamics of a molecular perturbation

simulate_experiment_no_transform: Link to the ode solver.

transform_params: User defined parameter transformation function.

Files in this package

pauwels2014
pauwels2014/inst
pauwels2014/inst/doc
pauwels2014/inst/doc/pauwels2014.pdf
pauwels2014/inst/doc/pauwels2014.Rnw
pauwels2014/inst/doc/pauwels2014.R
pauwels2014/src
pauwels2014/src/src.c
pauwels2014/src/model0_simplified_mrna_rates.c
pauwels2014/NAMESPACE
pauwels2014/data
pauwels2014/data/experiment_list1.rda
pauwels2014/data/knobjs.rda
pauwels2014/data/observables.rda
pauwels2014/data/exps.rda
pauwels2014/data/datalist
pauwels2014/R
pauwels2014/R/estimate_risk_out_all.R pauwels2014/R/risk_theta_vect.R pauwels2014/R/active_design.R pauwels2014/R/sample_function_multi_mod_weight.R pauwels2014/R/compute_gradient.R pauwels2014/R/sample_function_single_mod.R pauwels2014/R/eval_kn_log_like.R pauwels2014/R/BFGS_special.R pauwels2014/R/bed-internal.R pauwels2014/R/generate_sample.R pauwels2014/R/simulate_experiment.R pauwels2014/R/estimate_risk_dream6.R pauwels2014/R/random_design.R pauwels2014/R/dmvnorm.R pauwels2014/R/compute_mean_risks.R pauwels2014/R/reverse_params.R pauwels2014/R/compute_gradient_coordinate.R pauwels2014/R/transform_params.R pauwels2014/R/log_prior.R pauwels2014/R/eval_log_like_knobj.R pauwels2014/R/armijo.R pauwels2014/R/add_infinitesimal.R pauwels2014/R/log_normalize.R pauwels2014/R/read_knobjs.R pauwels2014/R/add_noise.R pauwels2014/R/sample_function.R pauwels2014/R/generate_our_knowledge.R pauwels2014/R/dream6_design.R pauwels2014/R/proj_grad.R pauwels2014/R/simulate_experiment_no_transform.R pauwels2014/R/risk_theta_fun.R pauwels2014/R/log_likelihood.R
pauwels2014/vignettes
pauwels2014/vignettes/refs.bib
pauwels2014/vignettes/pauwels2014.Rnw
pauwels2014/MD5
pauwels2014/build
pauwels2014/build/vignette.rds
pauwels2014/DESCRIPTION
pauwels2014/man
pauwels2014/man/sample_function_single_mod.Rd pauwels2014/man/proj_grad.Rd pauwels2014/man/log_likelihood.Rd pauwels2014/man/knobjs.Rd pauwels2014/man/exps.Rd pauwels2014/man/reverse_params.Rd pauwels2014/man/generate_our_knowledge.Rd pauwels2014/man/armijo.Rd pauwels2014/man/log_normalize.Rd pauwels2014/man/pauwels2014-package.Rd pauwels2014/man/risk_theta_vect.Rd pauwels2014/man/BFGS_special.Rd pauwels2014/man/log_prior.Rd pauwels2014/man/eval_log_like_knobj.Rd pauwels2014/man/active_design.Rd pauwels2014/man/simulate_experiment_no_transform.Rd pauwels2014/man/random_design.Rd pauwels2014/man/eval_kn_log_like.Rd pauwels2014/man/estimate_risk_out_all.Rd pauwels2014/man/add_noise.Rd pauwels2014/man/add_infinitesimal.Rd pauwels2014/man/experiment_list1.Rd pauwels2014/man/risk_theta_fun.Rd pauwels2014/man/sample_function_multi_mod_weight.Rd pauwels2014/man/observables.Rd pauwels2014/man/read_knobjs.Rd pauwels2014/man/simulate_experiment.Rd pauwels2014/man/transform_params.Rd pauwels2014/man/generate_sample.Rd pauwels2014/man/sample_function.Rd pauwels2014/man/estimate_risk_dream6.Rd pauwels2014/man/compute_gradient_coordinate.Rd pauwels2014/man/dream6_design.Rd pauwels2014/man/dmvnorm.Rd pauwels2014/man/compute_mean_risks.Rd pauwels2014/man/compute_gradient.Rd

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