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Found 5 papers, 5 papers with code
A deep learning pipeline for cross-sectional and longitudinal multiview data integration
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 in meaningfully integrating these diverse views.
mvlearnR and Shiny App for multiview learning
For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device.
Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction
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 more classes.
Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking -- An Application to COVID-19 severity
COVID-19 severity is due to complications from SARS-Cov-2 but the clinical course of the infection varies for individuals, emphasizing the need to better understand the disease at the molecular level.
sJIVE: Supervised Joint and Individual Variation Explained
We propose a method called supervised joint and individual variation explained (sJIVE) that can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures.
