Seyoung Kim
Seyoung Kim
Associate Professor of Computational Biology, Carnegie Mellon University
Geverifieerd e-mailadres voor cs.cmu.edu
Geciteerd door
Geciteerd door
Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
S Kim, EP Xing
Smoothing proximal gradient method for general structured sparse regression
X Chen, Q Lin, S Kim, JG Carbonell, EP Xing
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
Statistical estimation of correlated genome associations to a quantitative trait network
S Kim, EP Xing
PLoS genetics 5 (8), e1000587, 2009
A multivariate regression approach to association analysis of a quantitative trait network
S Kim, KA Sohn, EP Xing
Bioinformatics 25 (12), i204-i212, 2009
Heterogeneous multitask learning with joint sparsity constraints
X Yang, S Kim, E Xing
Advances in neural information processing systems 22, 2009
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
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
Learning gene networks under SNP perturbations using eQTL datasets
L Zhang, S Kim
PLoS computational biology 10 (2), e1003420, 2014
Multi-population GWA mapping via multi-task regularized regression
K Puniyani, S Kim, EP Xing
Bioinformatics 26 (12), i208-i216, 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
A* Lasso for learning a sparse Bayesian network structure for continuous variables
J Xiang, S Kim
Advances in neural information processing systems 26, 2013
Segmental Hidden Markov Models with Random Effects for Waveform Modeling.
S Kim, P Smyth, S Roweis
Journal of Machine Learning Research 7 (6), 2006
Hierarchical Dirichlet processes with random effects
S Kim, P Smyth
Advances in Neural Information Processing Systems 19, 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
An efficient proximal gradient method for general structured sparse learning
X Chen, Q Lin, S Kim, JG Carbonell, EP Xing
stat 1050, 2010
Modeling waveform shapes with random eects segmental hidden Markov models
S Kim, P Smyth, S Luther
arXiv preprint arXiv:1207.4143, 2012
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
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
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
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