| Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network J Bae, J Stocks, A Heywood, Y Jung, L Jenkins, V Hill, A Katsaggelos, ... Neurobiology of aging 99, 53-64, 2021 | 55 | 2021 |
| Transfer learning for predicting conversion from mild cognitive impairment to Dementia of Alzheimer’s type based on 3D-convolutional neural network J Bae, J Stocks, A Heywood, Y Jung, L Jenkins, A Katsaggelos, K Popuri, ... bioRxiv, 2019.12. 20.884932, 2019 | 7 | 2019 |
| A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression J Bae, PE Logan, DJ Acri, A Bharthur, K Nho, AJ Saykin, SL Risacher, ... Alzheimer's & Dementia 19 (12), 5690-5699, 2023 | 4 | 2023 |
| Grant report on PREDICT-ADFTD: multimodal imaging prediction of ad/ftd and differential diagnosis L Wang, A Heywood, J Stocks, J Bae, D Ma, K Popuri, AW Toga, ... Journal of psychiatry and brain science 4, 2019 | 3 | 2019 |
| P4‐588: END‐TO‐END 3D‐CONVOLUTIONAL NEURAL NETWORK FOR PREDICTING CONVERSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DEMENTIA J Bae, J Stocks, A Heywood, Y Jung, P Karteek, MF Beg, L Wang Alzheimer's & Dementia 15, P1547-P1547, 2019 | 1 | 2019 |
| Transforming genetic data for a convolutional neural network using genotype posterior probability: Algorithm for handling incorrectly imputed multiallelic variants for multiple … J Bae, KNH Nudelman, LG Apostolova Alzheimer's & Dementia 19, e078342, 2023 | | 2023 |
| Unbiased deep learning assessment of APOE4 risk for sporadic Alzheimer’s disease J Bae, K Nho, AJ Saykin, V Penchev, LG Apostolova Alzheimer's & Dementia 19, e067916, 2023 | | 2023 |
| Chromosome 19 polygenic risk scores defined by deep learning model predict the rate of Alzheimer’disease progression J Bae, AB Sanjay, K Nho, AJ Saykin, V Penchev, LG Apostolova Alzheimer's & Dementia 19, e068204, 2023 | | 2023 |
| Identification of SNP interactions in chromosome 19 for Alzheimer’s Disease through Capsule Network J Bae, K Nho, AJ Saykin, SL Risacher, V Penchev, LG Apostolova Alzheimer's & Dementia 19, e068129, 2023 | | 2023 |
| Computational CRISPR deep learning simulation of the likelihood of Alzheimer’s disease using chromosome 19 GWAS J Bae, DJ Acri, K Nho, AJ Saykin, V Penchev, LG Apostolova Alzheimer's & Dementia 19, e067915, 2023 | | 2023 |
| Relating occlusion maps obtained through deep learning to functional impairment in dementia of Alzheimer’s type: Neuroimaging/Optimal neuroimaging measures for early detection J Bae, J Stocks, A Heywood, Y Jung, A Katsaggelos, LM Jenkins, ... Alzheimer's & Dementia 16, e043538, 2020 | | 2020 |
| Relating Occlusion maps obtained through deep learning to functional impairment in Dementia of Alzheimer’s Type J Bae, JK Stocks, A Heywood, Y Jung, A Katsaggelos, LM Jenkins, ... 2020 Alzheimer's Association International Conference, 2020 | | 2020 |
| IC‐P‐128: SEX DIFFERENCES IN THE RELATIONSHIP BETWEEN CORTICAL NEURODEGENERATION AND FDG‐PET HYPOMETABOLISM IN AD AND PROGRESSIVE MCI J Stocks, J Bae, O Sangha, K Popuri, MF Beg, L Wang Alzheimer's & Dementia 15, P105-P106, 2019 | | 2019 |
| P2‐356: SEX DIFFERENCES IN THE RELATIONSHIP BETWEEN CORTICAL NEURODEGENERATION AND FDG‐PET HYPOMETABOLISM IN AD AND PROGRESSIVE‐MCI J Stocks, J Bae, O Sangha, K Popuri, MF Beg, L Wang Alzheimer's & Dementia 15, P734-P735, 2019 | | 2019 |
