38 citations · 59 across the 4 of their papers we have counts for
4 papers
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…
Permutation tests for the equality of covariance operators of functional data with applications to evolutionary biology
Alessandra Cabassi, Davide Pigoli, Piercesare Secchi +1
In this paper, we generalize the metric-based permutation test for the equality of covariance operators proposed by Pigoli et al. (2014) to the case of multiple samples of function…