| Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data J Yan, SL Risacher, L Shen, AJ Saykin Briefings in bioinformatics 19 (6), 1370-1381, 2018 | 334 | 2018 |
| Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer’s disease P Baloni, CC Funk, J Yan, JT Yurkovich, A Kueider-Paisley, K Nho, ... Cell Reports Medicine 1 (8), 2020 | 141 | 2020 |
| Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method L Du, H Huang, J Yan, S Kim, SL Risacher, M Inlow, JH Moore, AJ Saykin, ... Bioinformatics 32 (10), 1544-1551, 2016 | 117 | 2016 |
| Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease J Wan, Z Zhang, J Yan, T Li, BD Rao, S Fang, S Kim, SL Risacher, ... 2012 IEEE Conference on Computer Vision and Pattern Recognition, 940-947, 2012 | 104 | 2012 |
| High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer's disease progression prediction H Wang, F Nie, H Huang, J Yan, S Kim, S Risacher, A Saykin, L Shen Advances in neural information processing systems 25, 2012 | 103 | 2012 |
| From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs H Wang, F Nie, H Huang, J Yan, S Kim, K Nho, SL Risacher, AJ Saykin, ... Bioinformatics 28 (18), i619-i625, 2012 | 79 | 2012 |
| Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm J Yan, L Du, S Kim, SL Risacher, H Huang, JH Moore, AJ Saykin, L Shen, ... Bioinformatics 30 (17), i564-i571, 2014 | 77 | 2014 |
| Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation-and nonlinearity-aware sparse Bayesian learning J Wan, Z Zhang, BD Rao, S Fang, J Yan, AJ Saykin, L Shen IEEE transactions on medical imaging 33 (7), 1475-1487, 2014 | 75 | 2014 |
| Progress in polygenic composite scores in Alzheimer’s and other complex diseases D Chasioti, J Yan, K Nho, AJ Saykin Trends in Genetics 35 (5), 371-382, 2019 | 73 | 2019 |
| Deep fusion of brain structure-function in mild cognitive impairment L Zhang, L Wang, J Gao, SL Risacher, J Yan, G Li, T Liu, D Zhu, ... Medical image analysis 72, 102082, 2021 | 66 | 2021 |
| 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 | 54 | 2017 |
| Cortical surface biomarkers for predicting cognitive outcomes using group l2, 1 norm J Yan, T Li, H Wang, H Huang, J Wan, K Nho, S Kim, SL Risacher, ... Neurobiology of aging 36, S185-S193, 2015 | 53 | 2015 |
| A novel structure-aware sparse learning algorithm for brain imaging genetics L Du, J Yan, S Kim, SL Risacher, H Huang, M Inlow, JH Moore, AJ Saykin, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 53 | 2014 |
| 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 | 43 | 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 | 41 | 2018 |
| Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts A Song, J Yan, S Kim, SL Risacher, AK Wong, AJ Saykin, L Shen, ... BioData mining 9, 1-8, 2016 | 36 | 2016 |
| Genome-wide association and interaction studies of CSF T-tau/Aβ42 ratio in ADNI cohort J Li, Q Zhang, F Chen, X Meng, W Liu, D Chen, J Yan, S Kim, L Wang, ... Neurobiology of aging 57, 247. e1-247. e8, 2017 | 35 | 2017 |
| 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 | 33 | 2016 |
| Genetic Interactions Explain Variance in Cingulate Amyloid Burden: An AV‐45 PET Genome‐Wide Association and Interaction Study in the ADNI Cohort J Li, Q Zhang, F Chen, J Yan, S Kim, L Wang, W Feng, AJ Saykin, H Liang, ... BioMed research international 2015 (1), 647389, 2015 | 33 | 2015 |
| 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 | 30 | 2017 |
