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Seyoung Kim
Seyoung Kim
Associate Professor of Computational Biology, Carnegie Mellon University
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
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
6252012
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
2732012
Statistical estimation of correlated genome associations to a quantitative trait network
S Kim, EP Xing
PLoS genetics 5 (8), e1000587, 2009
2492009
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
2452008
A multivariate regression approach to association analysis of a quantitative trait network
S Kim, KA Sohn, EP Xing
Bioinformatics 25 (12), i204-i212, 2009
1922009
Heterogeneous multitask learning with joint sparsity constraints
X Yang, S Kim, E Xing
Advances in neural information processing systems 22, 2009
1012009
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
982012
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
952010
Multi-population GWA mapping via multi-task regularized regression
K Puniyani, S Kim, EP Xing
Bioinformatics 26 (12), i208-i216, 2010
622010
Learning gene networks under SNP perturbations using eQTL datasets
L Zhang, S Kim
PLoS computational biology 10 (2), e1003420, 2014
582014
Hierarchical Dirichlet processes with random effects
S Kim, P Smyth
Advances in Neural Information Processing Systems 19, 2006
462006
Segmental Hidden Markov Models with Random Effects for Waveform Modeling.
S Kim, P Smyth, S Roweis
Journal of Machine Learning Research 7 (6), 2006
452006
A* Lasso for learning a sparse Bayesian network structure for continuous variables
J Xiang, S Kim
Advances in neural information processing systems 26, 2013
432013
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, 228, 2018
422018
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
322017
An efficient proximal gradient method for general structured sparse learning
X Chen, Q Lin, S Kim, JG Carbonell, EP Xing
stat 1050, 2010
322010
An efficient proximal-gradient method for single and multi-task regression with structured sparsity
X Chen, Q Lin, S Kim, J Pena, JG Carbonell, EP Xing
stat 1050, 26, 2010
272010
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
S Kim, P Smyth, S Luther
arXiv preprint arXiv:1207.4143, 2012
262012
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
262010
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
252006
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Articles 1–20