8 citations · 13 across the 9 of their papers we have counts for
4 papers · 1 filter
Bayesian integrative factor analysis methods, with application in nutrition and genomics data
Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco +1
High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models…
Multi-study factor regression model: an application in nutritional epidemiology
Roberta De Vito, Alejandra Avalos-Pacheco
Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns is critical to reveal important associations with health. However, it i…
Bayesian Multi-study Factor Analysis for High-throughput Biological Data
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
This paper presents a new modeling strategy for joint unsupervised analysis of multiple high-throughput biological studies. As in Multi-study Factor Analysis, our goals are to iden…
Multi-study Factor Analysis
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate 1) common factors shared acr…