ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2018
Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.The annals of applied statistics · 2025Article
- Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.Journal of molecular neuroscience : MN · 2022Article
- Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease.Journal of the American Statistical Association · 2022Article
- Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method.IEEE transactions on medical imaging · 2020Article
- Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.Neuroinformatics · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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9 authors.
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Abstract
Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) 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 analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework 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. Using the
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