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Fused multi-modal similarity network as prior in guiding brain imaging genetic association

Frontiers in big data, 2023-05, Vol.6, p.1151893-1151893

2023

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Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease

Medical image analysis, 2020-02, Vol.60, p.101625-101625, Article 101625

2020

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Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment

Medical image analysis, 2021-08, Vol.72, p.102082-102082, Article 102082

2021

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Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach

Medical image analysis, 2020-04, Vol.61, p.101656-101656, Article 101656

2020

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Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

IEEE transactions on medical imaging, 2020-11, Vol.39 (11), p.3416-3428

2020

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Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Bioinformatics, 2019-07, Vol.35 (14), p.i474-i483

2019

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Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning

Bioinformatics (Oxford, England), 2012-06, Vol.28 (12), p.i127-i136

2012

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Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification

Bioinformatics (Oxford, England), 2020-07, Vol.36 (Supplement_1), p.i371-i379

2020

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Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method

Bioinformatics, 2016-05, Vol.32 (10), p.1544-1551

2016

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Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort

Bioinformatics, 2012-01, Vol.28 (2), p.229-237

2012

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Regional imaging genetic enrichment analysis

Bioinformatics, 2020-04, Vol.36 (8), p.2554-2560

2020

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Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 2010, Vol.13 (Pt 3), p.611-618

2010

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Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm

Bioinformatics, 2014-09, Vol.30 (17), p.i564-i571

2014

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Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis

Bioinformatics (Oxford, England), 2017-07, Vol.33 (14), p.i341-i349

2017

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Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics

IEEE/ACM transactions on computational biology and bioinformatics, 2021-01, Vol.18 (1), p.227-239

2021

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Quantitative trait loci identification for brain endophenotypes via new additive model with random networks

Bioinformatics, 2018-09, Vol.34 (17), p.i866-i874

2018

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From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs

Bioinformatics, 2012-09, Vol.28 (18), p.i619-i625

2012

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Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Bioinformatics (Oxford, England), 2017-10, Vol.33 (20), p.3250-3257

2017

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Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning

IEEE/ACM transactions on computational biology and bioinformatics, 2019-11, Vol.16 (6), p.1986-1996

2019

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Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Neuroinformatics (Totowa, N.J.), 2016-10, Vol.14 (4), p.439-452

2016

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Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model

Information Processing in Medical Imaging, 2017-01, Vol.10265, p.198-209

2017

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Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via a Novel Structured SCCA Approach

Information Processing in Medical Imaging, 2017-06, Vol.10265, p.543-555

2017

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DIAGNOSIS-GUIDED METHOD FOR IDENTIFYING MULTI-MODALITY NEUROIMAGING BIOMARKERS ASSOCIATED WITH GENETIC RISK FACTORS IN ALZHEIMER'S DISEASE

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016, Vol.21, p.108

2016

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Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, 2011, Vol.14 (Pt 2), p.376-383

2011

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A Novel Structure-Aware Sparse Learning Algorithm for Brain Imaging Genetics

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 2014, Vol.17 (Pt 3), p.329-336

2014

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Fused multi-modal similarity network as prior in guiding brain imaging genetic association

Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease

Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment

Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach

Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning

Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification

Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method

Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort

Regional imaging genetic enrichment analysis

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm

Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis

Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics

Quantitative trait loci identification for brain endophenotypes via new additive model with random networks

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning

Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model

Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via a Novel Structured SCCA Approach

DIAGNOSIS-GUIDED METHOD FOR IDENTIFYING MULTI-MODALITY NEUROIMAGING BIOMARKERS ASSOCIATED WITH GENETIC RISK FACTORS IN ALZHEIMER'S DISEASE

Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models

A Novel Structure-Aware Sparse Learning Algorithm for Brain Imaging Genetics

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