Kath Nicholls

This paper introduces DPMUnc, a Bayesian clustering method that incorporates data uncertainty, and applies it to GWAS and gene expression data to identify disease subgroups.

This paper introduces a semi-supervised Bayesian mixture model for joint batch correction and classification, showing improved performance on low-dimensional data compared to existing methods.

This study compares sparse biclustering algorithms on simulated and real datasets, finding Bayesian algorithms with sparsity constraints perform well on simulated data but are slow, while post-processed non-negative matrix factorization algorithms show strong performance on real datasets.