Peer Reviewed Publications
  • Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel and Daniela Witten. Learning Graphical Models with Hubs. To appear in the Journal of Machine Learning Research (JMLR) [arxiv link]
  • Safiye Celik, Benjamin A Logsdon and Su-In Lee (2014). Efficient Dimensionality Reduction for High-Dimensional Network Estimation, International Conference on Machine Learning (ICML 2014). acceptance rate 22%  [PDF] [appendix
    • Maxim Grechkin and Su-In Lee (2013). Identifying Perturbed Genes in the Regulatory Networks from Gene Expression Data. NIPS Workshop on Machine Learning in Computational Biology. Oral presentation (acceptance rate: 20%)
    • Safiye Celik, Benjamin A. Logsdon, and Su-In Lee (2013). Sparse Estimation of Module Gaussian Graphical Models with Applications to Cancer Systems Biology. NIPS Workshop on Machine Learning in Computational Biology. Oral presentation (acceptance rate: 20%).
      • K. Mohan, P. London, M. Fazel, D. Witten, S.-I. Lee (2013), Node-Based Learning of Multiple Gaussian Graphical Models, Journal of Machine Learning Research (JMLR). In Press. [arxiv link] [software]
      • E. Mercan, L. Shapiro, S. Weinberg, S.-I. Lee (2013). The Use of Pseudo-Landmarks for Craniofacial Analysis: A Comparative Study with L1-Regularized Logistic Regression. The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13).
      • K. Mohan, M. Chung, S. Han, D. Witten, S.-I. Lee, M. Fazel (2012). Structured Sparse Learning of Multiple Gaussian Graphical Models. To appear in Neural Information Processing Systems (NIPS). (acceptance rate: 25%) [PDF]
      • S.M. Schwartz, H.T. Schwartz, S. Horvath, E. Schadt, S.-I. Lee (2012). A Systematic Approach to Multifactorial Cardiovascular Disease: Causal Analysis. Accepted to Arteriosclerosis, Thrombosis, and Vascular Biology.
      • S. Yang, L. Shapiro, M. Cunningham, M. Speltz, C. Birgfeld, I. Atmosukarto, S.-I. Lee (2012). Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. Proceedings of the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). (acceptance rate: 30%) [PDF]  - best paper award
      • B. Soran, J.-N. Hwang, S.-I. Lee, L. Shapiro (2012). Tremor Detection Using Motion Filter and SVM. Proceedings of the 21st International Conference on Pattern Recognition. (acceptance rate: 50%) [PDF
      • R.P. Patwardhan, J.B. Hiatt, D.M. Witten, M.J. Kim, R.P. Smith, D. May, C. Lee, J.M. Andrie, S.-I. Lee, G.M. Cooper, N. Ahituv, L.A. Pennacchio, J. Shendure (2012). Massively parallel functional dissection of mammalian enhancers in vivo. Nature Biotechnology, 30(3), 265-70. [PDF] [Link]
      • B. Soran, Z. Xie, R. Tungaraza, S.-I. Lee, L. Shapiro, T. Grabowski (2012). Parcellation of Human Inferior Parietal Lobule Based On Diffusion MRI. Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Engineering Innovation in Global Health.  - selected for oral presentation. [PDF]
      • S. Yang, L. Shapiro, M.L. Cunningham, M. Speltz, S.-I. Lee (2011). Classification and Interest Region Localization on Craniosynostosis Skulls. Proceedings of ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB). (acceptance rate: 28%) [PDF]
      • I.M. Dykes, L. Tempest, S.-I. Lee, E. Turner (2011). Brn3a and Islet1 act epistatically to regulate the gene expression program of sensory differentiation. Journal of Neuroscience, 31(27), 9789-99. [PDF] [Link]
      • S. Balakrishnan, H. Kamisetty, J.C. Carbonell, S.-I. Lee, C.J. Langmead (2011). Learning Generative Models for Protein Fold Families. PROTEINS: Structure, Function, and Bioinformatics, 79(4), 1061-78. [PDF] [Link]
      • S. Balakrishnan, H. Kamisetty, J.C. Carbonell, S.-I. Lee, C.J. Langmead (2010). Learning Networks of Statistical Couplings in Protein Fold Families using L1-regularization. Proceedings of 3DSIG Structural Bioinformatics and Computational Biophysics.
      • A.J. Gentles, A.A. Alizadeh, S.-I. Lee, J.H. Myklebust, B. Shahbaba, C.M. Shachaf, R. Levy, D. Koller, S.K. Plevritis (2009). A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients. Blood, 114(15), 3133-4. [PDF]
      • S.-I. Lee, A.M. Dudley, D. Drubin, P.A. Silver, N.J. Krogan, D. Pe’er, D. Koller (2009). Learning a Prior on Regulatory Potential from eQTL Data. PLoS Genetics, 5(1), e1000358. [PDF] [Link]
      • S.-I. Lee, V. Chatalbashev, D. Vickrey, D. Koller (2007). Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks. Proceedings of International Conference on Machine Learning (ICML). (acceptance rate: 20%) [PDF]
      • S.-I. Lee, V. Ganapathi, D. Koller (2007). Efficient Structure Learning of Markov Networks using L1-Regularization. Proceedings of Neural Information Processing Systems (NIPS). (acceptance rate: 24%) [PDF]
      • S.-I. Lee, D. Pe’er, A.M. Dudley, G.M. Church, D. Koller (2006). Identifying Regulatory Mechanisms using Individual Variation Reveals Key Role for Chromatin Modification. Proceedings of the National Academy of Sciences (PNAS), 103, 14062-14067. [PDF] [Pubmed]
      • S.-I. Lee, H. Lee, P. Abbeel, A.Y. Ng (2006). Efficient L1 Regularized Logistic Regression. Proceedings of the 21th National Conference on Artificial Intelligence (AAAI).  (acceptance rate: 21%)  [PDF]
      • J.E. Galagan, S.E. Calvo, C. Cuomo, L.-J. Ma, J.R. Wortman, S.Batzoglou, S.-I. Lee, et al (2005). Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae. Nature, 438(7071), 1105-1115.
      • S.-I. Lee, S. Batzoglou (2004). ICA-based Clustering of Genes from Microarray Expression Data. Proceedings of Neural Information Processing Systems (NIPS).  (acceptance rate: 27.6%)
      • S.-I. Lee, S. Batzoglou (2003). Application of Independent Component Analysis to Microarrays. Genome Biology, 4(11), R76. [PDF]
      • S.-I. Lee, S.-Y. Lee (2000). Top-Down Attention Control at Feature Selection. Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision (BMCV). [Bibtex]
      • S.-I. Lee, S.-Y. Lee (2000). Biologically Inspired Neural Network Approach using Feature Extraction and Top-Down Selective Attention for Robust Optical Character Recognition. Proceedings of Humantech Paper Competition held by Samsung Electronics, Inc.. Gold prize (the first prize)