ArticleBioinformatics (Oxford, England)2018
A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- 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
- Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform.Annals of medicine · 2024Article
- Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.Proceedings of machine learning research · 2024Article
- 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
- Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.Interdisciplinary sciences, computational life sciences · 2021Article
- Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study.Bioinformatics (Oxford, England) · 2020Article
- 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
- Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach.Medical image analysis · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Genetic Variants Detection Based on Weighted Sparse Group Lasso.Frontiers in genetics · 2020Article
- Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.Bioinformatics (Oxford, England) · 2019Article
- DIAGNOSIS STATUS GUIDED BRAIN IMAGING GENETICS VIA INTEGRATED REGRESSION AND SPARSE CANONICAL CORRELATION ANALYSIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis.Neuroinformatics · 2018Article
- Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts.Neuroinformatics · 2018Article
- 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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Authors and funding
11 authors.
Funding
Abstract
motivationBrain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants to induce sparsity. The ℓ0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem.
resultsIn this paper, we propose the truncated ℓ1-norm penalized SCCA to improve the performance and effectiveness of the ℓ1-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ. It can avoid the time intensive parameter tuning if given a reasonable small τ. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 Socratic graph.