Seyoung Kim
Seyoung Kim
Assistant Professor of Computational Biology, Carnegie Mellon University
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Smoothing proximal gradient method for general structured sparse regression
X Chen, Q Lin, S Kim, JG Carbonell, EP Xing
The Annals of Applied Statistics 6 (2), 719-752, 2012
3082012
Statistical estimation of correlated genome associations to a quantitative trait network
S Kim, EP Xing
PLoS Genet 5 (8), e1000587, 2009
2272009
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
2142008
A multivariate regression approach to association analysis of a quantitative trait network
S Kim, KA Sohn, EP Xing
Bioinformatics 25 (12), i204-i212, 2009
1632009
Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
S Kim, EP Xing
The Annals of Applied Statistics 6 (3), 1095-1117, 2012
1212012
Heterogeneous multitask learning with joint sparsity constraints
X Yang, S Kim, EP Xing
Advances in neural information processing systems, 2151-2159, 2009
872009
Joint estimation of structured sparsity and output structure in multiple-output regression via inverse-covariance regularization
KA Sohn, S Kim
Artificial Intelligence and Statistics, 1081-1089, 2012
852012
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
832010
Learning gene networks under SNP perturbations using eQTL datasets
L Zhang, S Kim
PLoS Comput Biol 10 (2), e1003420, 2014
552014
Multi-population GWA mapping via multi-task regularized regression
K Puniyani, S Kim, EP Xing
Bioinformatics 26 (12), i208-i216, 2010
552010
Hierarchical Dirichlet processes with random effects
S Kim, P Smyth
Advances in Neural Information Processing Systems, 697-704, 2007
442007
Segmental hidden Markov models with random effects for waveform modeling
S Kim, P Smyth
Journal of Machine Learning Research 7 (Jun), 945-969, 2006
442006
A* Lasso for learning a sparse Bayesian network structure for continuous variables
J Xiang, S Kim
Advances in neural information processing systems, 2418-2426, 2013
322013
An efficient proximal gradient method for general structured sparse learning
X Chen, Q Lin, S Kim, JG Carbonell, EP Xing
stat 1050, 2010
302010
An efficient proximal-gradient method for single and multi-task regression with structured sparsity
X Chen, Q Lin, S Kim, EP Xing
stat 1050, 26, 2010
252010
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
242010
A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data
S Kim, P Smyth, H Stern
International Conference on Medical Image Computing and Computer-Assisted …, 2006
242006
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
S Kim, P Smyth, S Luther
arXiv preprint arXiv:1207.4143, 2012
222012
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
212018
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
212017
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Articles 1–20