ArticleMedical image analysis2020
Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Interpretable multimodal learning for integrating neuroimaging and genetic data in Alzheimer's disease.Frontiers in radiology · 2026Article
- Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.Bioinformatics (Oxford, England) · 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
- Integrating multi-omics data of childhood asthma using a deep association model.Fundamental research · 2024Article
- dCCA: detecting differential covariation patterns between two types of high-throughput omics data.Briefings in bioinformatics · 2024Article
- Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.BMC bioinformatics · 2024Article
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning.Human brain mapping · 2024Article
- Tumor radiogenomics in gliomas with Bayesian layered variable selection.Medical image analysis · 2023Article
- Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.Bioinformatics (Oxford, England) · 2023Article
- inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics.Genomics, proteomics & bioinformatics · 2023Article
- Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information.Frontiers in genetics · 2023Article
- Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method.BMC bioinformatics · 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
- Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.Journal of molecular neuroscience : MN · 2022Article
- Identification of Pathogenetic Brain RegionsFrontiers in neuroscience · 2022Article
- GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
- Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints.Medical & biological engineering & computing · 2022Article
- The exploration of Parkinson's disease: a multi-modal data analysis of resting functional magnetic resonance imaging and gene data.Brain imaging and behavior · 2021Article
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8 authors.
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Abstract
Brain imaging genetics becomes an important research topic since it can reveal complex associations between genetic factors and the structures or functions of the human brain. Sparse canonical correlation analysis (SCCA) is a popular bi-multivariate association identification method. To mine the complex genetic basis of brain imaging phenotypes, there arise many SCCA methods with a variety of norms for incorporating different structures of interest. They often use the group lasso penalty, the fused lasso or the graph/network guided fused lasso ones. However, the group lasso methods have limited capability because of the incomplete or unavailable prior knowledge in real applications. The fused lasso and graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. In this paper, we introduce two new penalties to improve the fused lasso and the graph/network guided lasso penalties in structured sparse learning. We impose both penalties to the SCCA model and propose an optimization algorithm to solve it. The proposed SCCA method has a strong upper bound of grouping effects for both positively and negatively highly correlated variables. We show that, on both synthetic and real neuroimaging genetics data, the proposed SCCA method performs better than or equally to the conventional methods using fused lasso or graph/network guided fused lasso. In particular, the proposed method identifies higher canonical correlation coefficients and captures clearer canonical weight patterns, demonstrating its promising capability in revealing biologically meaningful imaging genetic associations.
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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.