ARTIVA-package

ARTIVA


This package generates Reversible Jump MCMC (RJ-MCMC) 
sampling for approximating the posterior distribution of a time varying 
regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model 
(for a detailed description of the algorithm, see Lebre et al. 
BMC Systems Biology, 2010). <p></p><p>Starting from time-course gene expression measurements for a gene of
interest (referred to as "target gene") and a set of genes (referred to
as "parent genes") which may explain the expression of the target gene, the
ARTIVA procedure identifies temporal segments for which a set of
interactions occur between the "parent genes" and the "target gene".
The time points that delimit the different temporal segments are referred to as changepoints (CP).</p>

inference

time series

graphical model

network inference

Reversible Jump MCMC (RJ-MCMC)sampling for approximating the posterior
distribution of a time varying regulatory network, under the Auto Regressive TIme VArying
(ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems
Biology, 2010). Starting from time-course gene expression measurements for a gene of
interest (referred to as "target gene") and a set of genes (referred to as "parent genes")
which may explain the expression of the target gene, the ARTIVA procedure identifies
temporal segments for which a set of interactions occur between the "parent genes" and the
"target gene". The time points that delimit the different temporal segments are referred to
as changepoints (CP).

original version 10 by Sophie Lebre

Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme
VArying) Model

ARTIVA-package function


Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme VArying) Model


ARTIVA-package: Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme VArying) Model

Description

Arguments

Details

References