5 papers
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
Jackie Rao, Paul D. W. Kirk
Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratifiction of patients or samples. However, the growth in availability of high-dimens…
A discomfort-informed adaptive Gibbs sampler for finite mixture models
Davide Fabbrico, Andi Q. Wang, Sebastiano Grazzi +5
Finite mixture models are frequently used to uncover latent structures in high-dimensional datasets (e.g.\ identifying clusters of patients in electronic health records). The infer…
Federated Variational Inference for Bayesian Mixture Models
Jackie Rao, Francesca L. Crowe, Tom Marshall +2
We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled 'divide and conquer' inferenc…
Annealed variational mixtures for disease subtyping and biomarker discovery
Emma Prevot, Rory Toogood, Filippo Pagani +1
Cluster analyses of high-dimensional data are often hampered by the presence of large numbers of variables that do not provide relevant information, as well as the perennial issue…
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop a…