mvlearnR and Shiny App for multiview learning

Author:

Palzer Elise F1,Safo Sandra E1ORCID

Affiliation:

1. University of Minnesota Division of Biostatistics and Health Data Science , , Minneapolis, Minnesota 55414, United States

Abstract

Abstract Summary 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/.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Genetics,Molecular Biology,Structural Biology

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