title: Protein construct storage: Bayesian variable selection and prediction with mixtures.
abstract: Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.
authors.affiliation: Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina, USA.
authors.forename: M A
authors.initials: MA
authors.lastname: Clyde
authors.forename: G
authors.initials: G
authors.lastname: Parmigiani
mesh_terms: D001499:Bayes Theorem; D004356:Drug Storage; D008956:Models, Chemical; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry* / Q000494:pharmacology*
chemical_list: D011506:Proteins