• NSF/ABI Innovation

  • Javad Hosseiniand Su-In Lee  (2016). Learning Sparse Gaussian Graphical Models with Overlapping Blocks.  To appear in Neural Information Processing Systems (NIPS) 2016.    [Paper] [GRAB website] [Github]

  • Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara H. Rendi, David R. Hawkins, and Su-In Lee*  (2016). Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.  Genome Medicine  2016 Jun 10;8(1):66.  
    [Paper] [INSPIRE website]

    • Maxim Grechkin, Benjamin A. Logsdon, Andrew J. Gentles, and Su-In Lee*  (2016).  Identifying Network Perturbation in Cancer.  
      PLOS Computational Biology  12(5): e1004888. 
      [Paper] [DISCERN website

    • Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, and Su-In Lee*  (2016).   ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.   Genome Biology  2016 Apr 30;17(1):82. 
      [Paper[ChromNet browser]   

    • Benjamin A. Logsdon, Andrew J. Gentles, Chris P. Miller, C. Anthony Blau, Pamela S. Becker, and Su-In Lee*  (2015).  Sparse expression bases in cancer reveal tumor drivers.  Nucleic Acids Research 10.1093/nar/gku1290.  
      [Paper] [SPARROW website (Software & Data)]
    • Benjamin Logsdon, Andrew Gentles, Chris Miller, C. Anthony Blau, Pamela Becker and Su-In Lee (2014). SPARROW: Identifying expression drivers in cancer expression data, NIPS Workshop on Machine Learning in Computational Biology. best scoring paper, oral presentation [Link]

    • Safiye Celik, Benjamin Logsdon and Su-In Lee (2014). Efficient Dimensionality Reduction for High-Dimensional Network EstimationInternational Conference on Machine Learning (ICML)acceptance rate 22%  [Paper

    • Maxim Grechkin, Maryam Fazel, Daniela Witten, and Su-In Lee  (2015).  Pathway Graphical Lasso.  The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).acceptance rate (oral presentation) 11.7% 
      [Paper] [Software & Data]

    • K. Mohan, P. London, M. Fazel, D. Witten, S.-I. Lee, Node-Based Learning of Multiple Gaussian Graphical Models, Journal of Machine Learning Research (JMLR). arxiv link.
    • K. Mohan, M. Chung, S. Han, D. Witten, S.-I. Lee, M. Fazel (2012). Structured Sparse Learning of Multiple Gaussian Graphical Models. Neural Information Processing Systems (NIPS). (acceptance rate: 25%) [PDF] [bibtex]

    • 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. [Link]

    • Visualization tool for genotype, expression and phenotype measurements from individuals.

    • 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] [bibtex]