Bayesian probabilistic network modeling from multiple independent replicates.
Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.
Department of Mathematics, Wake Forest University, Winston-Salem, North Carolina 27109, USA.
Kristopher L
KL
Patton
David J
DJ
John
James L
JL
Norris
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D009010:Monte Carlo Method; D010766:Phosphorylation; D011336:Probability; D011506:Proteins / Q000737:chemistry*
D011506:Proteins