Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

Molecular cloning, characterization and localization of chicken type II procollagen gene

Chicken type II procollagen (ccol2a1) has become as an important oral tolerance protein for effective treatment of rheumatoid arthritis. However, its molecular identity remains unclear. Here, we reported the full-length cDNA and nearly complete genomic DN