Su-In Lee

Associate Professor of Genome Sciences and of Computer Science
Adjunct Associate Professor of Electrical Engineering


phone: (206) 685-1418
Paul Allen Center 536, Foege S-243C
suinlee [ a t ] cs.washington.edu
website

Research:

My group is broadly interested in developing computational methods to solve important problems in genetics and molecular biology. The goal of current research projects can be summarized as:

  • Inferring causal pathways from genetic and environmental influences to complex phenotypes (e.g. diseases) from DNA, phenotype and various functional genomic/proteomic information.

  • Developing the computational framework for personalized medicine in cancer.

  • Building probabilistic models representing various levels of gene regulation.

Our lab has close collaborations with bio- departments at the UW and nearby bio- institutions such as UW Medicine, Genome Sciences, Epidemiology, UW Medical Center, Fred Hutchinson Cancer Research center, Allen Institute for Brain Sciences, and Seattle Children's Hospital. This enables us to access to a lot of various types of high-throuput data including genotype, microarray, RNA-seq, exome-sequencing, proteomic data and various epigenomic profiles. These short-distance collaborations also facilitate experimental or clinical validation of the hypotheses generated by our computational models, which will amplify the impact of our approaches.

Selected Publications:

A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients. Andrew J. Gentles*, Ash A. Alizadeh*, Su-In Lee, June H. Myklebust, Babak Shahbaba, Catherine M. Shachaf, Ronald Levy, Daphne Koller, Sylvia K. Plevritis. Blood. 2009 Oct 8; 114(15):3133-4.

Learning a Prior on Regulatory Potential from eQTL Data. Su-In Lee, Aimee M. Dudley, David Drubin, Pamela A. Silver, Nevan J. Krogan, Dana Pe'er and Daphne Koller. PLoS Genet 5(1): e1000358. doi:10.1371/journal.pgen.1000358

Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks. Su-In Lee, Vassil Chatalbashev, David Vickrey and Daphne Koller. In Proceedings of International Conference on Machine Learning (ICML 2007), Corvallis, OR, June 2007.

Efficient Structure Learning of Markov Networks using L1-Regularization. Su-In Lee, Varun Ganapathi and Daphne Koller. In Proceedings of Neural Information Processing Systems (NIPS 19, 2007), Vancouver, British Columbia, December 2006.

Identifying Regulatory Mechanisms using Individual Variation Reveals Key Role for Chromatin Modification. Su-In Lee*, Dana Pe'er*, Aimee M. Dudley, George M. Church and Daphne Koller. Proceedings of the National Academy of Sciences (PNAS), 2006 Sep; 103: 14062-14067.

Efficient L1 Regularized Logistic Regression. Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), Boston, MA, USA, July 2006.

Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae. James E. Galagan, Sarah E. Calvo, Christina Cuomo, Li-Jun Ma, Jennifer R. Wortman, Serafim Batzoglou, Su-In Lee, et al. Nature, 2005 Dec; 438(7071): 1105-1115.

ICA-based Clustering of Genes from Microarray Expression Data. Su-In Lee and Serafim Batzoglou. In Proceedings of Neural Information Processing Systems (NIPS 16, 2004), Vancouver, British Columbia, December 2003.

Application of Independent Component Analysis to Microarrays. Su-In Lee and Serafim Batzoglou. Genome Biology, 2003 Oct; 4(11):R76.