9 citations · 9 across the 5 of their papers we have counts for
5 papers
Extensions of Heterogeneity in Integration and Prediction (HIP) with R Shiny Application
J. Butts, C. Wendt, R. Bowler +4
Multiple data views measured on the same set of participants is becoming more common and has the potential to deepen our understanding of many complex diseases by analyzing these d…
Explanations of Black-Box Models based on Directional Feature Interactions
Aria Masoomi, Davin Hill, Zhonghui Xu +5
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. Several recent works explain bl…
Robust Integrative Biclustering for Multi-view Data
W. Zhang, C. Wendt, R. Bowler +2
In many biomedical research, multiple views of data (e.g., genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized b…
Accounting for data heterogeneity in integrative analysis and prediction methods: An application to Chronic Obstructive Pulmonary Disease
J. Butts, C. Wendt, R. Bowler +4
Epidemiologic and genetic studies in chronic obstructive pulmonary disease (COPD) and many complex diseases suggest subgroup disparities (e.g., by sex). We consider this problem fr…
sJIVE: Supervised Joint and Individual Variation Explained
Elise F. Palzer, Christine Wendt, Russell Bowler +3
Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to u…