14 citations · 17 across the 8 of their papers we have counts for
5 papers · 1 filter
A deep learning pipeline for cross-sectional and longitudinal multiview data integration
Sarthak Jain, Sandra E. Safo
Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies…
mvlearnR and Shiny App for multiview learning
Elise F. Palzer, Sandra E. Safo
The package mvlearnR and accompanying Shiny App is intended for integrating data from multiple sources or views or modalities (e.g. genomics, proteomics, clinical and demographic d…
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…
Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction
Sandra E. Safo, Han Lu
We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or m…
Interpretable Deep Learning Methods for Multiview Learning
Hengkang Wang, Han Lu, Ju Sun +1
Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview…