ArticleNeuroImage2012
Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.
Article in NeuroImage, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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.
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Who cites it
71 citing papers in PubMed.
- Deep learning based imaging data completion for improved brain disease diagnosis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2014Trial
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- 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
- Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation.Bulletin of the Technical Committee on Data Engineering · 2024Article
- Latent relation shared learning for endometrial cancer diagnosis with incomplete multi-modality medical images.iScience · 2024Article
- Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies.Mathematics (Basel, Switzerland) · 2024Article
- Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.Psychometrika · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Sample-wise Combined Missing Effect Model with Penalization.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery.Frontiers in genetics · 2023Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.Current genomics · 2021Review
- Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images.IEEE transactions on medical imaging · 2021Article
- High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis.Frontiers in neuroscience · 2021Article
- A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.IISE transactions · 2021Article
- A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.BioMed research international · 2021Article
- Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.IEEE transactions on cybernetics · 2020Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.
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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.