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Found 6 papers, 4 papers with code
Bayesian outcome-guided multi-view mixture models with applications in molecular precision medicine
Clustering is commonly performed as an initial analysis step for uncovering structure in 'omics datasets, e. g. to discover molecular subtypes of disease.
Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?
We consider the Bayesian mixture of finite mixtures (MFMs) and Dirichlet process mixture (DPM) models for clustering.
Topological Approximate Bayesian Computation for Parameter Inference of an Angiogenesis Model
Here we focus on recent work using topological data analysis to study different regimes of parameter space for a well-studied model of angiogenesis.
Kernel learning approaches for summarising and combining posterior similarity matrices
Here we build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian clustering models.
Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome
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 differing characteristics of the data layers in terms of number of predictors, type of data, and levels of noise.
Multiple kernel learning for integrative consensus clustering of 'omic datasets
KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering.
