Protein construct storage: Bayesian variable selection and prediction with mixtures.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Clyde, MA; Parmigiani, G

Published Date

  • July 1998

Published In

Volume / Issue

  • 8 / 3

Start / End Page

  • 431 - 443

PubMed ID

  • 9741858

Pubmed Central ID

  • 9741858

Electronic International Standard Serial Number (EISSN)

  • 1520-5711

International Standard Serial Number (ISSN)

  • 1054-3406

Digital Object Identifier (DOI)

  • 10.1080/10543409808835251

Language

  • eng