| Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans AJ Saykin, L Shen, X Yao, S Kim, K Nho, SL Risacher, VK Ramanan, ... Alzheimer's & Dementia 11 (7), 792-814, 2015 | 307 | 2015 |
| Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease X Hao, Y Bao, Y Guo, M Yu, D Zhang, SL Risacher, AJ Saykin, X Yao, ... Medical image analysis 60, 101625, 2020 | 183 | 2020 |
| Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach L Du, K Liu, X Yao, SL Risacher, J Han, AJ Saykin, L Guo, L Shen Medical image analysis 61, 101656, 2020 | 78 | 2020 |
| Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort L Du, K Liu, L Zhu, X Yao, SL Risacher, L Guo, AJ Saykin, L Shen, ... Bioinformatics 35 (14), i474-i483, 2019 | 60 | 2019 |
| Mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in Alzheimer’s disease X Hao, C Li, L Du, X Yao, J Yan, SL Risacher, AJ Saykin, L Shen, ... Scientific reports 7 (1), 44272, 2017 | 51 | 2017 |
| A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics L Du, K Liu, T Zhang, X Yao, J Yan, SL Risacher, J Han, L Guo, AJ Saykin, ... Bioinformatics 34 (2), 278-285, 2018 | 44 | 2018 |
| Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis X Hao, C Li, J Yan, X Yao, SL Risacher, AJ Saykin, L Shen, D Zhang, ... Bioinformatics 33 (14), i341-i349, 2017 | 42 | 2017 |
| Multi-task sparse canonical correlation analysis with application to multi-modal brain imaging genetics L Du, K Liu, X Yao, SL Risacher, J Han, AJ Saykin, L Guo, L Shen IEEE/ACM transactions on computational biology and bioinformatics 18 (1 …, 2019 | 40 | 2019 |
| Associating multi-modal brain imaging phenotypes and genetic risk factors via a dirty multi-task learning method L Du, F Liu, K Liu, X Yao, SL Risacher, J Han, AJ Saykin, L Shen IEEE transactions on medical imaging 39 (11), 3416-3428, 2020 | 39 | 2020 |
| Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene X Yao, SL Risacher, K Nho, AJ Saykin, Z Wang, L Shen, ... Neurobiology of aging 81, 213-221, 2019 | 37 | 2019 |
| Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer’s disease X Hao, X Yao, J Yan, SL Risacher, AJ Saykin, D Zhang, L Shen, ... Neuroinformatics 14, 439-452, 2016 | 34 | 2016 |
| Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification L Du, F Liu, K Liu, X Yao, SL Risacher, J Han, L Guo, AJ Saykin, L Shen, ... Bioinformatics 36 (Supplement_1), i371-i379, 2020 | 31 | 2020 |
| Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules X Yao, J Yan, K Liu, S Kim, K Nho, SL Risacher, CS Greene, JH Moore, ... Bioinformatics 33 (20), 3250-3257, 2017 | 29 | 2017 |
| Privacy-preserving lof outlier detection L Li, L Huang, W Yang, X Yao, A Liu Knowledge and Information Systems 42, 579-597, 2015 | 28 | 2015 |
| Fast multi-task SCCA learning with feature selection for multi-modal brain imaging genetics L Du, K Liu, X Yao, SL Risacher, J Han, L Guo, AJ Saykin, L Shen 2018 IEEE international conference on bioinformatics and biomedicine (BIBM …, 2018 | 22 | 2018 |
| Regional imaging genetic enrichment analysis X Yao, S Cong, J Yan, SL Risacher, AJ Saykin, JH Moore, L Shen, ... Bioinformatics 36 (8), 2554-2560, 2020 | 20 | 2020 |
| Mining and visualizing high-order directional drug interaction effects using the FAERS database X Yao, T Tsang, Q Sun, S Quinney, P Zhang, X Ning, L Li, L Shen BMC medical informatics and decision making 20, 1-11, 2020 | 20 | 2020 |
| Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort W Cong, X Meng, J Li, Q Zhang, F Chen, W Liu, Y Wang, S Cheng, X Yao, ... BMC genomics 18, 1-14, 2017 | 20 | 2017 |
| Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative X Yao, J Yan, M Ginda, K Börner, AJ Saykin, L Shen, ... PLoS One 12 (11), e0186095, 2017 | 18 | 2017 |
| Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach L Du, T Zhang, K Liu, J Yan, X Yao, SL Risacher, AJ Saykin, J Han, L Guo, ... International Conference on Information Processing in Medical Imaging, 543-555, 2017 | 18 | 2017 |
Li ShenProfessor of Informatics and Radiology, University of Pennsylvania Perelman School of MedicinePatvirtintas el. paštas pennmedicine.upenn.edu