Gibbs Sampling Based Bayesian Biclustering Of Gene Expression Data

This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.