ArticleProceedings of the National Academy of Sciences of the United States of America2022
Cooperative learning for multiview analysis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- A multi-omics study reveals pathway-level insights and predictive biomarkers in pediatric TB.Clinical proteomics · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- A three-groups non-local model for combining heterogeneous data sources to identify genes associated with Parkinson's disease.Biometrics · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification.Computational and structural biotechnology journal · 2026Article
- A collaborative multi-view fusion framework with Deep Forest for multi-class diagnosis of Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.Data science in science · 2026Article
- A guide to network analysis, multi-omics integration, and applications in livestock microbiome research.World journal of microbiology & biotechnology · 2025Review
- Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Omic Risk Scores are Associated with COPD-related Traits Across Three Cohorts.medRxiv : the preprint server for health sciences · 2025Article
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
- The need for a cancer exposome atlas: a scoping review.JNCI cancer spectrum · 2025Article
- A machine learning approach to leveraging electronic health records for enhanced omics analysis.Nature machine intelligence · 2025Article
- Multimodal deep learning model for enhanced early detection of aortic stenosis integrating ECG and chest x-ray with cooperative learning.Frontiers in radiology · 2025Article
- Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
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4 authors.
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Abstract
We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, proteomics, and radiomics, are measured on a common set of samples. "Cooperative learning" combines the usual squared-error loss of predictions with an "agreement" penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g., lasso, random forests, boosting, or neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and real multiomics examples of labor-onset prediction. By leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.
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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.