| Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping S Kim, EP Xing Annals of Applied Statistics 6 (3), 1095-1117, 2012 | 671 | 2012 |
| Smoothing proximal gradient method for general structured sparse regression X Chen, Q Lin, S Kim, JG Carbonell, EP Xing Annals of Applied Statistics 6 (2), 719-752, 2012 | 306 | 2012 |
| Statistical estimation of correlated genome associations to a quantitative trait network S Kim, EP Xing PLoS genetics 5 (8), e1000587, 2009 | 274 | 2009 |
| Test–retest and between‐site reliability in a multicenter fMRI study L Friedman, H Stern, GG Brown, DH Mathalon, J Turner, GH Glover, ... Human brain mapping 29 (8), 958-972, 2008 | 274 | 2008 |
| A multivariate regression approach to association analysis of a quantitative trait network S Kim, KA Sohn, EP Xing Bioinformatics 25 (12), i204-i212, 2009 | 215 | 2009 |
| Heterogeneous multitask learning with joint sparsity constraints X Yang, S Kim, E Xing Advances in neural information processing systems (NeurIPS) 22, 2009 | 111 | 2009 |
| Joint estimation of structured sparsity and output structure in multiple-output regression via inverse-covariance regularization KA Sohn, S Kim International Conference on Artificial Intelligence and Statistics (AISTATS …, 2012 | 108 | 2012 |
| Graph-structured multi-task regression and an efficient optimization method for general fused lasso X Chen, S Kim, Q Lin, JG Carbonell, EP Xing arXiv preprint arXiv:1005.3579, 2010 | 106 | 2010 |
| Learning gene networks under SNP perturbations using eQTL datasets L Zhang, S Kim PLoS computational biology 10 (2), e1003420, 2014 | 66 | 2014 |
| Multi-population GWA mapping via multi-task regularized regression K Puniyani, S Kim, EP Xing Bioinformatics 26 (12), i208-i216, 2010 | 64 | 2010 |
| Machine learning and radiogenomics: lessons learned and future directions J Kang, T Rancati, S Lee, JH Oh, SL Kerns, JG Scott, R Schwartz, S Kim, ... Frontiers in oncology 8, 228, 2018 | 57 | 2018 |
| A* Lasso for learning a sparse Bayesian network structure for continuous variables J Xiang, S Kim Advances in neural information processing systems (NeurIPS) 26, 2013 | 52 | 2013 |
| Segmental Hidden Markov Models with Random Effects for Waveform Modeling. S Kim, P Smyth, S Roweis Journal of Machine Learning Research 7 (6), 945–969, 2006 | 46 | 2006 |
| Hierarchical Dirichlet processes with random effects S Kim, P Smyth Advances in Neural Information Processing Systems (NeurIPS) 19, 2006 | 45 | 2006 |
| Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization S Kim, S Oesterreich, S Kim, Y Park, GC Tseng Biostatistics 18 (1), 165-179, 2017 | 36 | 2017 |
| An efficient proximal gradient method for general structured sparse learning X Chen, Q Lin, S Kim, JG Carbonell, EP Xing arXiv, 2010 | 33 | 2010 |
| A Bayesian mixture approach to modeling spatial activation patterns in multisite fMRI data S Kim, P Smyth, H Stern IEEE transactions on medical imaging 29 (6), 1260-1274, 2010 | 29 | 2010 |
| Modeling waveform shapes with random eects segmental hidden Markov models S Kim, P Smyth, S Luther arXiv preprint arXiv:1207.4143, 2012 | 28 | 2012 |
| A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data S Kim, P Smyth, H Stern Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006: 9th …, 2006 | 25 | 2006 |
| On sparse Gaussian chain graph models C McCarter, S Kim Advances in Neural Information Processing Systems (NeurIPS) 27, 2014 | 19 | 2014 |
