Preprints (under review)

Scott M. Lundberg, Bala Nair, Monica S. Vavilala, Mayumi Horibe, Michael J. Eisses, Trevor Adams, David E. Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kimand Su-In Lee*.  
Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery.
[Paper in bioRxiv]   In Revision   Nature BME

Nao HiranumaScott M. Lundbergand Su-In Lee*.  
AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification.
[Paper in bioRxiv]    In Revision Nucleic Acids Research 

Scott M. LundbergGabriel Erion, and Su-In Lee.
Consistent Individualized Feature Attribution for Tree Ensembles.

Ayse DincerSafiye CelikNao Hiranuma, and Su-In Lee*
DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia 

Hugh ChenScott M. Lundberg, and Su-In Lee.
Checkpoint Ensembles: Ensemble Methods from a Single Training Process. 

Prof. Lee in her office writing 
paper (photo: Dennis Wise)

Safiye Celik, Josh C. Russell, Shubhabrata Mukherjee, Paul K. Crane, C. Dirk Keene, Jennifer F. Bobb, Matt Kaeberlein*, and Su-In Lee*
A computational framework identifying concordant gene expression-neuropathology associations reveals Complex I as a potential Alzheimer’s disease therapeutic target.

Lee lab members, Corresponding author*, Co-first authorsC

Refereed Journal Publications

Su-In Lee*,CSafiye CelikC, Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian M. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Akanksha Saxena, C. Anthony Blau, and Pamela S. Becker. 
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Nature Communications 9, Article number: 42 

afiye CelikBenjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara H. Rendi, David R. Hawkins, and 
Su-In Lee*
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]
  • Featured in Casey S. Greene. The future is unsupervised. Science Translational Medicine 06 Jul 2016, Vol. 8 (346), pp. 346ec108  Editors' Choice  [Link]
Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, and Su-In Lee*
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.
Genome Biology  2016 Apr 30;17(1):82. 
  • Recommended in F1000Prime as being of special significance in its field: Access the recommendation on F1000Prime 

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

C. Anthony Blau, Arturo B. Ramirez, Sibel Blau, Colin C. Pritchard, Michael O. Dorschner, Stephen C. Schmechel, Timothy J. Martins, Elisabeth M. Mahen, Kimberly A. Burton, Vitalina M. Komashko, Amie J. Radenbaugh, Katy Dougherty, Anju Thomas, Christopher P. Miller, James Annis, Jonathan R. Fromm, Chaozhong Song, Elizabeth Chang, Kellie Howard, Sharon Austin, Rodney A. Schmidt, Michael L. Linenberger, Pamela S. Becker, Francis M. Senecal, Brigham H. Mecham, Su-In Lee, Anup Madan, Roy Ronen, Janusz Dutkowski, Shelly Heimfeld, Brent L. Wood, Jackie L. Stilwell, Eric P. Kaldjian, David Haussler, and Jingchun Zhu.
A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple Negative Breast Cancer. 
Journal of the National Comprehensive Cancer Network. 2016 Jan;14(1):8 - 17.  

Robert T. Lawrence, Elizabeth M. Perez, Daniel Hernández, Chris P. Miller, Kelsey M. Haas, Hanna Y. Irie, Su-In Lee, C. Anthony Blau, and Judit Villén (2015). 
The Proteomic Landscape of Triple-Negative Breast Cancer.  
Cell Reports Volume 11, Issue 4, p630–644, 28.  

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)]

Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, and Daniela Witten (2014). 
Learning Graphical Models with Hubs.
Journal of Machine Learning Research (JMLR) 15(Oct):3297 - 3331.

Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, and Su-In Lee (2014). 
Node-Based Learning of Multiple Gaussian Graphical Models.
 Journal of Machine Learning Research (JMLR) 15(Feb):445 - 488.  
[Paper] [Software]

Stephen M. Schwartz*, Hillel T. Schwartz, Steve Horvath, Eric Schadt, and Su-In Lee (2012). 
A Systematic Approach to Multifactorial Cardiovascular Disease: Causal Analysis
Arteriosclerosis, Thrombosis, and Vascular Biology, 32(12):2821-35.  

Rupali P. Patwardhan, Joseph B. Hiatt, Daniela M. Witten, Mee J. Kim, Robin P. Smith, Dalit May, Choli Lee, Jennifer M. Andrie, Su-In Lee, Gregory M. Cooper, Nadav Ahituv*, Len A. Pennacchio*, and Jay Shendure* (2012). 
Massively parallel functional dissection of mammalian enhancers in vivo.
Nature Biotechnology, 30(3), 265-70.  
[Paper] [Link]

Iain M. Dykes, Lynne Tempest, Su-In Leeand Eric E. Turner* (2011). 
Brn3a and Islet1 act epistatically to regulate the gene expression program of sensory differentiation.
Journal of Neuroscience, 31(27), 9789-99. 
[Paper] [Link]

Sivaraman Balakrishnan, Hetunandan Kamisetty, Jaime G. Carbonell, Su-In Leeand Christopher J. Langmead* (2011).
Learning Generative Models for Protein Fold Families. 
PROTEINS: Structure, Function, and Bioinformatics, 79(4), 1061-78.  
[Paper] [Link]

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

Su-In Lee, Aimee M. Dudley, David Drubin, Pamela A. Silver, Nevan J. Krogan, Dana Pe’er, and Daphne Koller* (2009). 
Learning a Prior on Regulatory Potential from eQTL Data.
PLOS Genetics, 5(1), e1000358.  
[Paper] [Software]

Su-In LeeC, Dana Pe’erC, Aimee M. Dudley, George M. Church, and Daphne 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.  

James E. Galagan, Sarah E. Calvo, Christina Cuomo, Li-Jun Ma, Jennifer R. Wortman, Sarafim Batzoglou, Su-In 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, and S. Batzoglou* (2003). 
Application of Independent Component Analysis to Microarrays.
Genome Biology, 4(11), R76.  

S.-I. Lee, and 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) 

Refereed Conference/Workshop Papers

Sumit Mukherjee, Alberto Carignano, Georg Seelig*, and Su-In Lee*.
Identifying progressive gene network perturbation from single-cell RNA-seq data.  IEEE Engineering in Medicine and Biology Society (EMBC) 2018.  [Paper in bioRxiv]

Scott M. Lundberg, and Su-In Lee
A unified approach to interpreting model predictions.  Neural Information Processing Systems (NIPS) 2017  Oral Presentation 
  • Selected for oral presentation (acceptance rate: 1.2%)
Gabriel ErionHugh ChenScott M. Lundberg, and Su-In Lee.
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning.  Neural Information Processing Systems (NIPS) 2017 Workshop ML4H: Machine Learning for Health 

Hugh ChenScott M. Lundberg, and Su-In Lee.
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data.  Neural Information Processing Systems (NIPS) 2017 Workshop ML4H: Machine Learning for Health

Nao Hiranuma, Scott Lundbergand Su-In Lee.
DeepATAC: A deep-learning method to estimate regulatory factor binding activity from ATAC-seq signals.  International Conference on Machine Learning (ICML) 2017 Workshop on Computational Biology

Scott Lundbergand Su-In Lee.
An unexpected unity among methods for interpreting model predictions.
Neural Information Processing Systems (NIPS) 2016 Workshop "Interpretable Machine Learning for Complex Systems."  
  • Received the Best Paper Award.

Javad Hosseiniand Su-In Lee . 
Learning Sparse Gaussian Graphical Models with Overlapping Blocks.
Neural Information Processing Systems (NIPS) 2016.  

Naozumi Hiranuma
Scott Lundberg
and Su-In Lee.
Cloud Control: Leveraging many public ChIP-seq control experiments to better remove background noise
The 7th ACM Conference on Bioinformatics, Computational Biology (ACM-BCB) 2016.  

Mohammad Javad Hosseini, Noah A. Smith, and Su-In Lee.
Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields
NAACL Workshop SemEval 2016 Task10: Detecting Minimal Semantic Units and their Meanings.  [Short Paper]

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% 

Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, and Su-In Lee  (2015).

Learning the human chromatin network from all ENCODE ChIP-seq data.  
NIPS Workshop on Machine Learning in Computational Biology acceptance rate (oral presentation) 20.9%  

Mohammad Javad Hosseini, and Su-In Lee  (2015).
Gaussian graphical models with overlapping blocks.  
NIPS Workshop on Machine Learning in Computational Biology acceptance rate (spotslight presentation) 30%  

Safiye CelikBenjamin Logsdon, and Su-In Lee (2014). 
Efficient Dimensionality Reduction for High-Dimensional Network Estimation. 
International Conference on Machine Learning (ICML)acceptance rate 22%  
[Paper] [Software]

Su-In Lee, Benjamin Logsdon, Akanksha Saxena, Vivian Oehler, Chris P. Miller, C. Anthony Blau, and Pamela Becker  (2014). 
Big Data Approach to Identify Molecular Basis for Drug Sensitivity Phenotypes in Acute Myeloid Leukemia.
American Society of Hematology Annual Meeting. acceptance rate (oral) 13%   Blood 124:265. [Link]

Benjamin Logsdon, Andrew Gentles, Chris Miller, C. Anthony Blau, Pamela Becker, and Su-In Lee (2014). 
Identifying expression drivers in gene expression data.
NIPS Workshop on Machine Learning in Computational Biologyacceptance rate (oral) 30%  best-scoring paper [Link]

Ezgi Mercan, Linda Shapiro, Seth Weinberg, and Su-In Lee  (2013). 
The Use of Pseudo-Landmarks for Craniofacial Analysis: A Comparative Study with L1-Regularized Logistic Regression. 
IEEE Engineering in Medicine and Biology Society (EMBC)acceptance rate (oral) 40%  PMID: 24111127  

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. acceptance rate (oral) 20.45%  [Link]

Safiye CelikBenjamin 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. acceptance rate (oral) 20.45%  [Link]

Ka Yee Yeung, C. Anthony Blau, Vivian Oehler, Su-In Lee, Christopher Miller, Sylvia Chien, Timothy Martins, Elihu Estey and Pamela Becker (2013). 
Personalized Approach To Treatment of Acute Myeloid Leukemia Using a High-Throughput Chemosensitivity Assay. 
American Society of Hematology Annual MeetingBlood 122 (21). acceptance rate (oral) 10%  [Link]

Min Fang, Scott McElhone, Xin Zhao, Barry Storer, Su-In Lee, C. Anthony Blau, Vivian Oehler, Elihu Estey, Frederick Appelbaum, and Pamela Becker (2013). 
Proof-Of-Concept Study For Precision Medicine With Chromosome Genomic Array Testing (CGAT) For Drug Sensitivity Screening In Acute Myeloid Leukemia. 
American Society of Hematology Annual MeetingBlood 122 (21).  acceptance rate (poster) 20%  [Link]

Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-In Lee, and Maryam Fazel (2012). 
Structured Learning of Gaussian Graphical Models. 
Neural Information Processing Systems (NIPS)acceptance rate 25%  
[Paper] [Software]

Shulin Yang, Linda Shapiro, Michael Cunningham, Matthew Speltz, Craig Birgfeld, Indriyati Atmosukarto, and Su-In Lee  (2012). 
Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.
Medical Image Computing and Computer Assisted Intervention (MICCAI). acceptance rate: 30%  best paper award

Bilge Soran, Jeng-Neng Hwang, Su-In Lee, and Linda Shapiro (2012). 
Tremor Detection Using Motion Filter and SVM.
International Conference on Pattern Recognition

Bilge Soran, Zhiyong Xie, Rosalia Tungaraza, Su-In Lee, Linda Shapiro, and Thomas Grabowski (2012). 
Parcellation of Human Inferior Parietal Lobule Based On Diffusion MRI.
IEEE Engineering in Medicine & Biology Society, Engineering Innovation in Global Health PMID: 23366611  

Shulin Yang, Linda Shapiro, Michael L. Cunningham, Matthew Speltz, and Su-In Lee (2011). 
Classification and Interest Region Localization on Craniosynostosis Skulls.
ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB)acceptance rate: 28% 

Sivaraman Balakrishnan, Hetunandan Kamisetty, Jaime G. Carbonell, Su-In Lee, and Christopher J. Langmead (2010). 
Learning Networks of Statistical Couplings in Protein Fold Families using L1-regularization.
Proceedings of 3DSIG Structural Bioinformatics and Computational Biophysics.

Su-In Lee, Vassil Chatalbashev, David Vickrey, and Daphne 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% 

Su-In Lee, Varun Ganapathi, and Daphne Koller (2007). 
Efficient Structure Learning of Markov Networks using L1-Regularization.
Proceedings of Neural Information Processing Systems (NIPS). acceptance rate: 24% 
[Paper] [Bibtex]

Su-In Lee, Honglak Lee, Pieter Abbeel, and Andrew Y. Ng (2006). 
Efficient L1 Regularized Logistic Regression.
Proceedings of the 21th National Conference on Artificial Intelligence (AAAI).  (acceptance rate: 21%) 
[Paper] [Bibtex] [Software]

Su-In Leeand Serafim Batzoglou (2004). 
ICA-based Clustering of Genes from Microarray Expression Data.
Proceedings of Neural Information Processing Systems (NIPS). (acceptance rate: 27.6%) 
[Paper] [Bibtex]

S.-I. Leeand S.-Y. Lee (2000). Top-Down Attention Control at Feature Selection.
Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision (BMCV)