ArticleComputational statistics & data analysis2022
sJIVE: Supervised Joint and Individual Variation Explained.
Article in Computational statistics & data analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Bayesian joint and individual component regression for multigroup physiological data.Scientific reports · 2026Article
- A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.Statistics in medicine · 2026Article
- Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.Statistics in medicine · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.PloS one · 2026Article
- The need for a cancer exposome atlas: a scoping review.JNCI cancer spectrum · 2025Article
- Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.Computational statistics & data analysis · 2024Article
- Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.bioRxiv : the preprint server for biology · 2024Article
- DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.Briefings in bioinformatics · 2024Article
- Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS.Journal of applied statistics · 2024Article
- Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers.Bioinformatics and biology insights · 2024Article
- mvlearnR and Shiny App for multiview learning.Bioinformatics advances · 2024Article
- Joint and Individual Component Regression.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Bayesian predictive modeling of multi-source multi-way data.Computational statistics & data analysis · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. The proposed method, supervised joint and individual variation explained (sJIVE), 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. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study explores gene expression and proteomic patterns associated with lung function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.