Probability Connectivity-Based Multimodality Regression Analysis for Associating Disease-Specific Multimodal Brain Imaging Phenotypes with Genetic Risk Factors
Author:
Affiliation:
1. Qufu Normal University,School of Computer Science and School of Engineering,Rizhao,Shandong,China
2. Qufu Normal University,School of Computer Science,Rizhao,Shandong,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995333.pdf?arnumber=9995333
Reference16 articles.
1. Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics
2. Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease
3. Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method
4. Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach
5. Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis
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