ArticleIEEE transactions on bio-medical engineering2018
Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- DNA methylation and brain structure and function across the life course: A systematic review.Neuroscience and biobehavioral reviews · 2020Pooled it
- Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders.Bioinformatics (Oxford, England) · 2026Article
- A multi-omics integration framework using multi-label guided learning and multi-scale fusion.Briefings in bioinformatics · 2025Article
- Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.Bioinformatics (Oxford, England) · 2025Article
- A generalized higher-order correlation analysis framework for multi-omics network inference.PLoS computational biology · 2025Article
- Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.Briefings in bioinformatics · 2025Article
- Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.Journal of molecular neuroscience : MN · 2025Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.Briefings in bioinformatics · 2024Article
- Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.IEEE transactions on medical imaging · 2024Article
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- On statistical inference with high-dimensional sparse CCA.Information and inference : a journal of the IMA · 2023Article
- 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
- Identify Biomarkers of Alzheimer's Disease Based on Multi-task Canonical Correlation Analysis and Regression Model.Journal of molecular neuroscience : MN · 2022Article
- Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis.Journal of molecular neuroscience : MN · 2022Article
- Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.IEEE transactions on medical imaging · 2021Article
- A Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study.IEEE transactions on medical imaging · 2021Article
- Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.IEEE journal of biomedical and health informatics · 2020Article
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7 authors.
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Abstract
Finding correlations across multiple data sets in imaging and (epi)genomics is a common challenge. Sparse multiple canonical correlation analysis (SMCCA) is a multivariate model widely used to extract contributing features from each data while maximizing the cross-modality correlation. The model is achieved by using the combination of pairwise covariances between any two data sets. However, the scales of different pairwise covariances could be quite different and the direct combination of pairwise covariances in SMCCA is unfair. The problem of "unfair combination of pairwise covariances" restricts the power of SMCCA for feature selection. In this paper, we propose a novel formulation of SMCCA, called adaptive SMCCA, to overcome the problem by introducing adaptive weights when combining pairwise covariances. Both simulation and real-data analysis show the outperformance of adaptive SMCCA in terms of feature selection over conventional SMCCA and SMCCA with fixed weights. Large-scale numerical experiments show that adaptive SMCCA converges as fast as conventional SMCCA. When applying it to imaging (epi)genetics study of schizophrenia subjects, we can detect significant (epi)genetic variants and brain regions, which are consistent with other existing reports. In addition, several significant brain-development related pathways, e.g., neural tube development, are detected by our model, demonstrating imaging epigenetic association may be overlooked by conventional SMCCA. All these results demonstrate that adaptive SMCCA are well suited for detecting three-way or multiway correlations and thus can find widespread applications in multiple omics and imaging data integration.
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