38 citations · 53 across the 10 of their papers we have counts for
10 papers
Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis
Luis A. Vargas-Mieles, Paul D. W. Kirk, Chris Wallace
Biclustering has gained interest in gene expression data analysis due to its ability to identify groups of samples that exhibit similar behaviour in specific subsets of genes (or v…
Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?
Yannis Chaumeny, Johan van der Molen Moris, Anthony C. Davison +1
We consider the Bayesian mixture of finite mixtures (MFMs) and Dirichlet process mixture (DPM) models for clustering. Recent asymptotic theory has established that DPMs overestimat…
Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables
Anaïs Rouanet, Rob Johnson, Magdalena E Strauss +4
The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelli…
Tailored Bayes: a risk modelling framework under unequal misclassification costs
Solon Karapanagiotis, Umberto Benedetto, Sach Mukherjee +2
Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology w…
Kernel learning approaches for summarising and combining posterior similarity matrices
Alessandra Cabassi, Sylvia Richardson, Paul D. W. Kirk
When using Markov chain Monte Carlo (MCMC) algorithms to perform inference for Bayesian clustering models, such as mixture models, the output is typically a sample of clusterings (…
Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome
Alessandra Cabassi, Denis Seyres, Mattia Frontini +1
Building classification models that predict a binary class label on the basis of high dimensional multi-omics datasets poses several challenges, due to the typically widely differi…
Multiple kernel learning for integrative consensus clustering of 'omic datasets
Alessandra Cabassi, Paul D. W. Kirk
Diverse applications - particularly in tumour subtyping - have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources…
Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics
Oliver M. Crook, Kathryn S. Lilley, Laurent Gatto +1
Understanding sub-cellular protein localisation is an essential component to analyse context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have…
Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics
Oliver M. Crook, Laurent Gatto, Paul D. W. Kirk
The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential upd…
Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data
Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin +2
We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowin…