Author

Kaida Ning

University of Southern CaliforniaORCID 0000-0002-9223-2014Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:GWAS / association mapping· 2 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Arthur W. TogaiD· 2 shared Fengzhu SuniD· 2 shared Lu ZhaoiD· 2 shared William J. MatloffiD· 2 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Ben A. DuffyiD· 1 shared Bozhen Ren· 1 shared Huiling LiuiD· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersNeuroscience

Arthur W. TogaORCID

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4 papers

Recent·Most cited

Multi-trait genome-wide analysis identified novel risk loci and candidate drugs for heart failure

Zhengyang Yu, Maohuan Lin, Zhanyu Liang, Bozhen Ren, Ying Yang, Xiaoling Lin, Huiling Liu, Yangxin Chen, Kaida Ning, Li C. Xia

This study identifies novel genetic risk loci and potential drugs for heart failure using multi-trait genome-wide analysis of GWAS data.

Human Genetics and Genomics Advances · Oct 30, 2025Peer-reviewedGenetic & Genomic Medicine

Characterizing brain imaging features associated with ADAS-Cog13 sub-scores with 3D convolutional neural networks

Kaida Ning, Pascale Bouchard-Cannon, Jiawei Yu, Srinesh Shenoi, Lu Wang, Alzheimer’s Disease Neuroimaging Initiative, Joydeep Sarkar

This study uses 3D CNNs to predict ADAS-Cog13 sub-scores from MRI and FDG-PET data, finding modality-specific brain features associated with cognitive changes in Alzheimer's disease.

bioRxiv · Mar 17, 2022 · PreprintNeuroscience

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

Kaida Ning, Ben A. Duffy, Meredith Franklin, William J. Matloff, Lu Zhao, Nibal Arzouni, Fengzhu Sun, Arthur W. Toga

This study improves brain age prediction using deep learning and identifies four genetic loci associated with brain aging, three of which are novel.

Neurobiology of Aging · Apr 20, 2021Peer-reviewedNeuroscience

Association of brain age with smoking, alcohol consumption, and genetic variants

Kaida Ning, Lu Zhao, William J. Matloff, Fengzhu Sun, Arthur W. Toga

This study investigates the association between brain age and smoking, alcohol consumption, and genetic variants using UK Biobank data, finding significant links with smoking and alcohol use but not genetic variants.

bioRxiv · Nov 14, 2018 · PreprintNeuroscience