134 citations · 375 across the 22 of their papers we have counts for
23 papers
Characterising mortality dynamics across countries and time using a multi-stage clustering approach
Pedro Menezes de Araújo, Ugofilippo Basellini, Thomas Brendan Murphy +1
Comparative analyses of mortality dynamics across countries have long shaped our understanding of mortality inequalities and patterns of divergence and convergence over time. Howev…
Summarising mortality data with a time-dependent beta latent variable model
Pedro Menezes de Araújo, Isobel Claire Gormley, Thomas Brendan Murphy
Age-specific probabilities of death provide a snapshot of population mortality at the country level at a given point in time. Due to the high dimensionality of the data, summarisin…
Clustering country-level all-cause mortality data: a review
Pedro Menezes de Araujo, Isobel Claire Gormley, Thomas Brendan Murphy
Mortality data are relevant to demography, public health, and actuarial science. Whilst clustering is increasingly used to explore patterns in such data, no study has reviewed its…
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…
A Latent Shrinkage Position Model for Binary and Count Network Data
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
Interactions between actors are frequently represented using a network. The latent position model is widely used for analysing network data, whereby each actor is positioned in a l…
A novel family of beta mixture models for the differential analysis of DNA methylation data: an application to prostate cancer
Koyel Majumdar, Romina Silva, Antoinette Sabrina Perry +5
Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysi…
Inferring food intake from multiple biomarkers using a latent variable model
Silvia D'Angelo, Lorraine Brennan, Isobel Claire Gormley
Metabolomic based approaches have gained much attention in recent years due to their promising potential to deliver objective tools for assessment of food intake. In particular, mu…
Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models
Keefe Murphy, Thomas Brendan Murphy, Raffaella Piccarreta +1
Sequence analysis is an increasingly popular approach for analysing life courses represented by ordered collections of activities experienced by subjects over time. Here, we analys…
Combining biomarker and self-reported dietary intake data: a review of the state of the art and an exposition of concepts
Isobel Claire Gormley, Yuxin Bai, Lorraine Brennan
Classical approaches to assessing dietary intake are associated with measurement error. In an effort to address inherent measurement error in dietary self-reported data there is in…
Mixtures of Experts Models
Isobel Claire Gormley, Sylvia Frühwirth-Schnatter
Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions…
Infinite Mixtures of Infinite Factor Analysers
Keefe Murphy, Cinzia Viroli, Isobel Claire Gormley
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data. Typically, the numbers of clusters and latent factors must…
Clustering high dimensional mixed data to uncover sub-phenotypes:joint analysis of phenotypic and genotypic data
Damien McParland, Catherine M. Phillips, Lorraine Brennan +2
The LIPGENE-SU.VI.MAX study, like many others, recorded high dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account…
Model Based Clustering for Mixed Data: clustMD
Damien McParland, Isobel Claire Gormley
A model based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussi…
Investigation of Parameter Uncertainty in Clustering Using a Gaussian Mixture Model Via Jackknife, Bootstrap and Weighted Likelihood Bootstrap
Adrian O'Hagan, Thomas Brendan Murphy, Luca Scrucca +1
Mixture models are a popular tool in model-based clustering. Such a model is often fitted by a procedure that maximizes the likelihood, such as the EM algorithm. At convergence, th…
Clustering South African households based on their asset status using latent variable models
Damien McParland, Isobel Claire Gormley, Tyler H. McCormick +3
The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a…
MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach
Gift Nyamundanda, Isobel Claire Gormley, Yue Fan +2
Background: Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample siz…
A dynamic probabilistic principal components model for the analysis of longitudinal metabolomic data
Gift Nyamundanda, Isobel Claire Gormley, Lorraine Brennan
In a longitudinal metabolomics study, multiple metabolites are measured from several observations at many time points. Interest lies in reducing the dimensionality of such data and…
A mixture of experts model for rank data with applications in election studies
Isobel Claire Gormley, Thomas Brendan Murphy
A voting bloc is defined to be a group of voters who have similar voting preferences. The cleavage of the Irish electorate into voting blocs is of interest. Irish elections employ…