ReviewNeuroImage2014
Bi-level multi-source learning for heterogeneous block-wise missing data.
Review in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Functional imaging constrained diffusion for brain PET synthesis from structural MRI.Medical image analysis · 2026Article
- Profile-guided Hybrid Approach for block-wise missing data handling in multi-omics: a breast cancer case study.BioData mining · 2026Article
- Accelerating real-world prediction and research in Alzheimer's: The M3AD study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.Journal of neural engineering · 2025Article
- Improving Regression Analysis with Imputation in a Longitudinal Study of Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2024Article
- A framework for block-wise missing data in multi-omics.PloS one · 2024Article
- Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.Psychometrika · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
- Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery.Frontiers in genetics · 2023Article
- Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.IEEE transactions on bio-medical engineering · 2021Article
- A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.IISE transactions · 2021Article
- High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis.Frontiers in neuroscience · 2021Article
- Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.IEEE transactions on cybernetics · 2020Article
- Generalized integrative principal component analysis for multi-type data with block-wise missing structure.Biostatistics (Oxford, England) · 2020Article
- Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.IEEE transactions on medical imaging · 2019Article
- Multi-View Cluster Analysis with Incomplete Data to Understand Treatment Effects.Information sciences · 2019Article
- Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.IEEE transactions on bio-medical engineering · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified "bi-level" learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches.
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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.