mlsanityData to 14 Oct 2026

mvlearnR and Shiny App for multiview learning

arXiv 2311.16181 ↗First posted 25 Nov 2023q-bio.GN cs.LG stat.APcited by 0

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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/

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.

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8 of 21 references.

References · 8

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  2. 2021sJIVE: Supervised Joint and Individual Variation Explainedcited by 15
  3. 2019Sparse Linear Discriminant Analysis for Multi-view Structured Datacited by 9
  4. 2016Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Datacited by 7
  5. 2021Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking -- An Application to COVID-19 severitycited by 5
  6. 2023Interpretable Deep Learning Methods for Multiview Learningcited by 5
  7. 2023Scalable Randomized Kernel Methods for Multiview Data Integration and Predictioncited by 2
  8. 2021Robust Integrative Biclustering for Multi-view Datacited by 1

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