ArticleIEEE transactions on bio-medical engineering2019
Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 51 citations in OpenAlex.
- A novel diagnosis method based on multimodal-aware bi-objective competitive mechanism for Alzheimer's disease.Scientific reports · 2026Article
- Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics.Computational and structural biotechnology journal · 2024Article
- Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.Behavior genetics · 2024Review
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.Machine learning in medical imaging. MLMI (Workshop) · 2022Article
- Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.IEEE transactions on cybernetics · 2022Article
- Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.Medical image analysis · 2022Article
- Discovery of Genetic Biomarkers for Alzheimer's Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies.Interdisciplinary sciences, computational life sciences · 2021Article
- Deep Learning-Enabled Resolution-Enhancement in Mini- and Regular Microscopy for Biomedical Imaging.Sensors and actuators. A, Physical · 2021Article
- A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.BioMed research international · 2021Article
- Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.IEEE journal of biomedical and health informatics · 2020Article
- Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.Medical image analysis · 2020Article
- A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions.Frontiers in genetics · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.IEEE transactions on medical imaging · 2019Article
- BIRNet: Brain image registration using dual-supervised fully convolutional networks.Medical image analysis · 2019Article
- Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.Human brain mapping · 2019Article
- Implementation strategy of a CNN model affects the performance of CT assessment of EGFR mutation status in lung cancer patients.IEEE access : practical innovations, open solutions · 2019Article
- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis.Predictive Intelligence in Medicine. PRIME (Workshop) · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants [i.e., single nucleotide polymorphism (SNP)] in Alzheimer's disease (AD). The main challenges of this study include the data heterogeneity, complex phenotype-genotype associations, high-dimensional data (e.g., thousands of SNPs), and the existence of phenotype outliers. Previous BW-GWA studies, while addressing some of these challenges, did not consider the diagnostic label information in their formulations, thus limiting their clinical applicability. To address these issues, we present a novel joint projection and sparse regression model to discover the associations between the phenotypes and genotypes. Specifically, to alleviate the negative influence of data heterogeneity, we first map the genotypes into an intermediate imaging-phenotype-like space. Then, to better reveal the complex phenotype-genotype associations, we project both the mapped genotypes and the original imaging phenotypes into a diagnostic-label-guided joint feature space, where the intraclass projected points are constrained to be close to each other. In addition, we use l
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Registered trials
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.