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Found 3 papers, 3 papers with code
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.
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