| Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data T Jo, K Nho, AJ Saykin Frontiers in aging neuroscience 11, 220, 2019 | 537 | 2019 |
| Improving Protein Fold Recognition by Deep Learning Networks T Jo, J Hou, J Eickholt, J Cheng Scientific reports 5, 17573, 2015 | 136 | 2015 |
| Deep learning detection of informative features in tau PET for Alzheimer’s disease classification T Jo, K Nho, SL Risacher, AJ Saykin, Alzheimer’s Neuroimaging Initiative BMC bioinformatics 21, 1-13, 2020 | 74 | 2020 |
| Improving protein fold recognition by random forest T Jo, J Cheng BMC bioinformatics 15, 1-7, 2014 | 64 | 2014 |
| Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification T Jo, K Nho, P Bice, AJ Saykin, ... Briefings in Bioinformatics 23 (2), bbac022, 2022 | 37 | 2022 |
| Deep Learning for Everyone T Jo Gilbut, 1-308, 2017 | 7 | 2017 |
| For the Alzheimer’s disease neuroimaging initiative deep learning-based identification of genetic variants: application to Alzheimer’s disease classification T Jo, K Nho, P Bice, AJ Saykin Briefings Bioinform 23 (2), bbac022, 2022 | 5 | 2022 |
| Evaluation of protein structural models using random forests R Cao, T Jo, J Cheng arXiv:1602.04277, 2016 | 5 | 2016 |
| Multimodal-CNN: Improved Accuracy of MRI-based Classification of Alzheimer’s Disease by Incorporating Clinical Data in Deep Learning T Jo, K Nho, SL Risacher, J Yan, AJ Saykin Alzheimer's & Dementia: The Journal of the Alzheimer's Association 14 (7), P1574, 2018 | 2* | 2018 |
| Homology Modeling of an Algal Membrane Protein, Heterosigma Akashiwo Na^+-ATPase T Jo, M Shono, M Wada, S Ito, J Nomoko, Y Hara Membrane 35 (2), 80-85, 2010 | 2 | 2010 |
| Multimodal-3DCNN: Diagnostic Classification of Alzheimer's Disease Using Deep Learning on Neuroimaging, Genetic, and Demographic Data T Jo, K Nho, SL Risacher, AJ Saykin Alzheimer's & Dementia: The Journal of the Alzheimer's Association 15 (7 …, 2019 | 1* | 2019 |
| A possible mechanism for low affinity of silkworm Na+/K+-ATPase for K+ H Homareda, M Otsu, S Yamamoto, M Ushimaru, S Ito, T Fukutomi, T Jo, ... J Bioenerg Biomembr, 463–472, 2017 | 1 | 2017 |
| Deep learning‐based SWAT‐TAB approach for Identifying Genetic Variants using Whole Genome Sequencing T Jo, K Nho, AJ Saykin Alzheimer's & Dementia 19, e079369, 2023 | | 2023 |
| Deep Learning‐based Integration of neuroimaging and genetic data for classification of Alzheimer’s Disease T Jo, K Nho, SL Risacher, AJ Saykin Alzheimer's & Dementia 19, e074318, 2023 | | 2023 |
| Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data T Jo, J Kim, P Bice, K Huynh, T Wang, M Arnold, PJ Meikle, C Giles, ... EBioMedicine 97, 2023 | | 2023 |
| Novel circling SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data T Jo, J Kim, P Bice, K Huynh, T Wang, PJ Meikle, R Kaddurah‐Daouk, ... Alzheimer's & Dementia 18, e069310, 2022 | | 2022 |
| Deep learning–based genome‐wide association analysis in Alzheimer’s disease T Jo, K Nho, AJ Saykin Alzheimer's & Dementia 17, e056510, 2021 | | 2021 |
| Deep learning detection of informative features in [18F] flortaucipir PET for Alzheimer’s disease classification: Neuroimaging/Optimal neuroimaging measures for early detection T Jo, K Nho, SL Risacher, AJ Saykin Alzheimer's & Dementia 16, e041126, 2020 | | 2020 |
| Deep Learning detection of informative features in [18F] Flortaucipir PET for Alzheimer’s disease classification T Jo, K Nho, SL Risacher, AJ Saykin 2020 Alzheimer's Association International Conference, 2020 | | 2020 |
Kwangsik NhoAssociate Professor of Radiology and Imaging Sciences, Indiana University School of MedicineVerified email at iupui.edu
