mvlearnR and Shiny App for multiview learning

Elise F. Palzer, Sandra E. Safo

Research output: Contribution to journalArticlepeer-review

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/.

Original languageEnglish (US)
Article numbervbae005
JournalBioinformatics Advances
Volume4
Issue number1
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
# The Author(s) 2024. Published by Oxford University Press.

PubMed: MeSH publication types

  • Journal Article

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