# mvlearnR and Shiny App for multiview learning

arXiv: https://arxiv.org/abs/2311.16181 · HTML: https://mlsanity.com/paper/2311.16181/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [sJIVE: Supervised Joint and Individual Variation Explained](https://mlsanity.com/paper/2102.13278/) (2021)
- [Sparse Linear Discriminant Analysis for Multi-view Structured Data](https://mlsanity.com/paper/1911.05643/) (2019)
- [Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data](https://mlsanity.com/paper/1611.01066/) (2016)
- [Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking -- An Application to COVID-19 severity](https://mlsanity.com/paper/2111.09964/) (2021)
- [Interpretable Deep Learning Methods for Multiview Learning](https://mlsanity.com/paper/2302.07930/) (2023)
- [Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction](https://mlsanity.com/paper/2304.04692/) (2023)
- [Robust Integrative Biclustering for Multi-view Data](https://mlsanity.com/paper/2111.06209/) (2021)

## Abstract (arXiv)

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 data). Most existing software packages for multiview learning are decentralized and offer limited capabilities, making it difficult for users to perform comprehensive integrative analysis. The new package wraps statistical and machine learning methods and graphical tools, providing a convenient and easy data integration workflow. For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device. The methods have potential to offer deeper insights into complex disease mechanisms. Availability and Implementation: mvlearnR is available from the following GitHub repository: https://github.com/lasandrall/mvlearnR. The web application is hosted on shinyapps.io and available at: https://multi-viewlearn.shinyapps.io/MultiView_Modeling/