Office of Research and Innovation

author keywords: expected information gain; logistic regression; linear and nonlinear regression; multifactor designs; Shannon information

TL;DR: A genetic algorithm can be used to find near-optimal Bayesia nexperimental designs for regression models based on the expected Shannon information gain from performing a given experiment compared with the prior distribution. (via Semantic Scholar)

MeSH headings : Anti-Inflammatory Agents, Non-Steroidal / pharmacology; Base Sequence; Binding Sites; COUP Transcription Factor I; Cell Nucleus / metabolism; Chromatin / metabolism; Cloning, Molecular; Cytokines / genetics; Cytokines / metabolism; DNA-Binding Proteins / metabolism; Gene Deletion; Genes, Reporter; Glutathione Transferase / metabolism; Growth Differentiation Factor 15; Humans; Models, Genetic; Molecular Sequence Data; Mutation; Plasmids / metabolism; Precipitin Tests; Promoter Regions, Genetic; Protein Binding; Protein Isoforms; Protein Structure, Tertiary; Recombinant Fusion Proteins / metabolism; Recombinant Proteins / metabolism; Sequence Homology, Nucleic Acid; Sp1 Transcription Factor / metabolism; Sp3 Transcription Factor; Transcription Factors / metabolism; Transcription, Genetic; Transfection

TL;DR: The results suggest that the level of expression of the NAG-1 gene will depend on the availability of Sp proteins and on co-factors such as chicken ovalbumin upstream promoter-transcription factor 1. (via Semantic Scholar)