ArticleBioinformatics (Oxford, England)2017
Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- Sparse Canonical Correlation Analysis for Multiple Measurements With Latent Trajectories.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Deep adversarial learning identifies ADHD-specific associations between apoptotic genes and white matter microstructure in frontal-striatum-cerebellum circuit.Translational psychiatry · 2025Article
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.Biomolecules · 2023Article
- Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.Current genomics · 2021Review
- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.IEEE transactions on bio-medical engineering · 2021Article
- A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects' age.Communications biology · 2021Article
- Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.EMBO molecular medicine · 2021Article
- The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.Aging and disease · 2020Review
- A recursive framework for predicting the time-course of drug sensitivity.Scientific reports · 2020Article
- Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment.Bioinformatics (Oxford, England) · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.Journal of biomedical informatics · 2019Article
- Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
motivationNeuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.
resultsThe proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer's Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://sourceforge.net/projects/ibrain-cn/files/ . CONTACT: dqzhang@nuaa.edu.cn or shenli@iu.edu.
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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 OpenQuestion graph.