2 citations · 5 across the 9 of their papers we have counts for
7 papers · 1 filter
Integrated differential analysis of multi-omics data using a joint mixture model: idiffomix
Koyel Majumdar, Florence Jaffrézic, Andrea Rau +2
Gene expression and DNA methylation are two interconnected biological processes and understanding their relationship is important in advancing understanding in diverse areas, inclu…
Missing data imputation using a truncated Gaussian infinite factor model with application to metabolomics data
Kate Finucane, Lorraine Brennan, Roberta De Vito +2
Metabolomics is the study of small molecules in biological samples. Metabolomics data are typically high-dimensional and contain highly correlated variables and frequent missing va…
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
Ganesh Babu, Aoife Gowen, Michael Fop +1
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to…
Variational Inference for the Latent Shrinkage Position Model
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
The latent position model (LPM) is a popular method used in network data analysis where nodes are assumed to be positioned in a -dimensional latent space. The latent shrinkage p…
Model-based Clustering for Network Data via a Latent Shrinkage Position Cluster Model
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assum…
Predicting milk traits from spectral data using Bayesian probabilistic partial least squares regression
Szymon Urbas, Pierre Lovera, Robert Daly +3
High-dimensional spectral data -- routinely generated in dairy production -- are used to predict a range of traits in milk products. Partial least squares (PLS) regression is ubiqu…