19 citations · 19 across the 7 of their papers we have counts for
9 papers
Modeling Time-course Gene Expression Data through Bayesian Partition Functional Principal Component Analysis
Marion Kerioui, Daniel Temko, Shahin Tavakoli +1
High-dimensional biomarkers such as gene expression levels are now routinely measured over time, allowing biological processes to be studied dynamically rather than through cross-s…
A scalable Bayesian functional factor model for high-dimensional longitudinal molecular data
Salima Jaoua, Daniel Temko, Hélène Ruffieux
Large-scale longitudinal molecular profiling is now firmly established in biomedical research, prompted by the need to uncover coordinated biomarker trajectories reflecting the dyn…
Adaptive Bayesian computation for efficient biobank-scale genomic inference
Yiran Li, John Whittaker, Sylvia Richardson +1
Motivation: Modern biobanks, with unprecedented sample sizes and phenotypic diversity, have become foundational resources for genomic studies, enabling powerful cross-phenotype and…
NExON-Bayes: A Bayesian approach to network estimation informed by ordinal covariates
Joseph Feest, Hélène Ruffieux, Camilla Lingjærde +1
In heterogeneous disease settings, accounting for intrinsic sample variability is crucial for obtaining reliable and interpretable omic network estimates. However, most graphical m…
Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves
Tui Nolan, Sylvia Richardson, Hélène Ruffieux
The analysis of multivariate functional curves has the potential to yield important scientific discoveries in domains such as healthcare, medicine, economics and social sciences. H…
A modelling framework for detecting and leveraging node-level information in Bayesian network inference
Xiaoyue Xi, Hélène Ruffieux
Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in…
Scalable Multiple Network Inference with the Joint Graphical Horseshoe
Camilla Lingjærde, Benjamin P. Fairfax, Sylvia Richardson +1
Network models are useful tools for modelling complex associations. If a Gaussian graphical model is assumed, conditional independence is determined by the non-zero entries of the…
A global-local approach for detecting hotspots in multiple-response regression
Hélène Ruffieux, Anthony C. Davison, Jörg Hager +4
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, i.e., predictors associated…
Efficient inference for genetic association studies with multiple outcomes
Hélène Ruffieux, Anthony C. Davison, Jörg Hager +1
Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. C…