ArticleIEEE/ACM transactions on computational biology and bioinformatics
Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics.
Article in IEEE/ACM transactions on computational biology and bioinformatics. 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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Who cites it
14 citing papers in PubMed.
- A Two-Stage Framework for Early Detection and Subtype Identification of Alzheimer's Disease Through Multimodal Biomarker Extraction and Improved GCN.Brain sciences · 2026Article
- Exploration of biomarkers of Alzheimer's disease based on orthogonal multi-task canonical correlation analysis.BMC medical imaging · 2025Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study.International journal of molecular sciences · 2024Article
- Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm.Journal of molecular neuroscience : MN · 2024Article
- inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics.Genomics, proteomics & bioinformatics · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Sparse Interpretation of Graph Convolutional Networks for Multi-Modal Diagnosis of Alzheimer's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method.BMC bioinformatics · 2022Article
- Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.Medical image analysis · 2022Article
- Identifying Imaging Genetics Biomarkers of Alzheimer's Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression.Frontiers in genetics · 2021Article
- Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method.IEEE transactions on medical imaging · 2020Article
- Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.Bioinformatics (Oxford, England) · 2020Article
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8 authors.
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Abstract
Brain imaging genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. MTSCCA enforces sparsity at the group level via the G
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