B AI in Basic Biology

 C AI in Clinical Medicine

  J     Journal publications

 C    Conference publications

 P    Perspectives & Reviews

* indicates equal contribution


 B   J   A deep profile of gene expression across 18 human cancers

Wei Qiu, Ayse Berceste Dincer, Joseph Janizek, Safiye Celik, Mikael Pittet, Kamila Naxerova* and Su-In Lee*


 B   C   Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI

Wei Qiu, Ethan Weinberger,  Su-In Lee


 B   C   Towards scalable embedding models for spatial transcriptomics data

Seo-Yoon Moon, Ethan Weinberger, Su-In Lee

Machine Learning in Computational Biology (MLCB), 2023


Selected Publications


 C   P    The clinical potential of counterfactual AI models

The Lancet


 C    J   Transparent medical image AI via an image-text foundational model grounded in medical literature

Chanwoo Kim, Soham U. Gadgil, Alex J. DeGrave, Jesutofunmi A. Omiye, Zhuo Ran Cai, Roxana Daneshjou*, and Su-In Lee*

In Press, Nature Medicine

medRxiv | Nature

 A   C   Estimating Conditional Mutual Information for Dynamic Feature Selection

Soham U. Gadgil*, Ian Covert*, Su-In Lee

Accepted, ICLR'24



 C    J   Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

Alex J. DeGrave, Zhuo Ran Cai, Joseph D. Janizek, Roxana Daneshjou*, and Su-In Lee*

Nature Biomedical Engineering

medRxiv | Nature | SharedIt 


 A   C   Feature selection in the contrastive analysis setting

Ethan Weinberger, Ian Covert, Su-In Lee

Neural Information Processing Systems (NeurIPS) 2023  


 A   C   On the robustness of removal-based feature attributions

Chris Lin*, Ian Covert*, Su-In Lee

Neural Information Processing Systems (NeurIPS) 2023  


 C    J   An explainable AI framework for interpretable biological age

Wei Qiu, Hugh Chen, Matt Kaeberlein, Su-In Lee

Lancet Healthy Longevity - featured on the cover

medRxiv | Lancet

 B    J   Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI

Ethan Weinberger*, Chris Lin*, Su-In Lee

Nature Methods  20, 1336–1345 (2023)  

bioRxiv | Nature

 A    J   Algorithms to estimate Shapley value feature attributions

Hugh Chen*, Ian Covert*, Scott Lundberg, Su-In Lee

Nature Machine Intelligence  (2023)

arXiv | PDF | Nature

 A   C   Learning to Maximize Mutual Information for Dynamic Feature Selection

Ian Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan White, Su-In Lee

International Conference on Machine Learning (ICML)


 B    J   Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models 

 Joseph D. Janizek, Ayse B. Dincer, Safiye Celik, Hugh Chen, William Chen, Kamila Naxerova*, Su-In Lee* 

Nature Biomedical Engineering  7, 811–829 (2023

bioRxiv  | Nature

 B    J   PAUSE: principled feature attribution for unsupervised gene expression analysis 

Joseph D. Janizek,  Anna Spiro, Safiye Celik, Ben W Blue, Josh C Russell, Ting-I Lee, Matt Kaeberlein, Su-In Lee. 

Genome Biology  24, Article number: 81 (2023) 

bioRxiv | PDF

 B    J   PERSIST: Predictive and robust gene selection for spatial transcriptomics  

Ian Covert, Rohan Gala, Tim Wang, Karel Svoboda, Uygar S ̈umb ̈ul*, Su-In Lee* 

Nature Communications  (2023)14:2091

bioRxiv | PDF

 A   C   Learning to estimate Shapley values with vision transformers

Ian Covert*, Chanwoo Kim*, Su-In Lee 

International Conference on Learning Representations (ICLR) spotlight

arXiv | OpenReview

 A   C   Contrastive Corpus Attribution for Explaining Representations  

Chris Lin*, Hugh Chen*, Chanwoo Kim, Su-In Lee

International Conference on Learning Representations (ICLR)  

arXiv | OpenReview


 C    J   IMPACT: Interpretable complex machine learning prediction of all-cause mortality

Wei Qiu, Hugh Chen, Ayse Berceste Dincer, Scott Lundberg, Matt Kaeberlein, Su-In Lee

Nature Communications Medicine 2, Article number: 125 

pdf | medRxiv

 C   P   Breaking into the black box of artificial intelligence 

Nature Outlook written by Neil Savage 


pdf | medRxiv

 A    J   Explaining a series of models by propagating local feature attributions

Hugh Chen, Scott M. Lundberg, Su-In Lee. 

Nature Communications 13, Article number: 4512 (2022)


 B    J   An automatic integrative method for learning interpretable communities of biological pathways

Nicasia Beebe-Wang, Ayse B. Dincer, Su-In Lee

Nucleic Acids Research (NAR) Genomics and Bioinformatics  Volume 4, Issue 2, June 2022, lqac044


 C    J   CoAI: Cost-Aware Artificial Intelligence for Health Care

Gabriel Erion, Joseph D. Janizek, Carly Hudelson, Richard B. Utarnachitt, Andrew M. McCoy, Michael R. Sayre, Nathan J. White*, Su-In Lee*

Nature Biomedical Engineering  2022 Apr 7;10.1038/s41551-022-00872-8

pdf | medRxiv


 A   C   FastSHAP: Real-Time Shapley Value Estimation

Neil Jethani*, Mukund Sudarshan*, Ian Covert*, Su-In Lee, Rajesh Ranganath

The International Conference on Learning Representations (ICLR) 2022  


 A   C   Moment Matching Deep Contrastive Latent Variable Models

Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee 

The International Conference on AI & Statistics (AISTATS) 2022  



 C    J   Forecasting adverse surgical events using self-supervised transfer learning for physiological signals

Hugh Chen, Scott M. Lundberg, Gabriel Erion, Jerry H. Kim, Su-In Lee 

Nature partner journal Digital Medicine 


 C   P   Course corrections for clinical AI

Alex J. DeGrave*, Joseph D. Janizek*, Su-In Lee

Kidney360 10.34067/KID.0004152021, September 2021 


 B   P   Reproducibility standards for machine learning in the life sciences

Benjamin J. Heil, Michael M. Hoffman, Florian Markowetz, Su-In Lee, Casey S. Greene*, Stephanie C. Hicks*

Nature Methods  1132–1135 (2021)  Comments


 B    J   Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies

Nicasia Beebe-Wang, Safiye Celik, Ethan Weinberger, Pascal Sturmfels, Philip L. De Jager, Sara Mostafavi,* Su-In Lee* 

Nature Communications  12, Article number: 5369 (2021) 



 C    J   Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images 

Parmita Mehta, Christine Petersen, Joanne C. Wen, Michael R. Banitt, Philip P. Chen, Karine D. Bojikian, Catherine Egan, Su-In Lee, Magdalena Balazinska, Aaron Y. Lee*, Ariel Rokem*, The UK Biobank Eye and Vision Consortium. 

American Journal of Ophthalmology   DOI:https://doi.org/10.1016/j.ajo.2021.04.021, May 2021 


 C    J   AI for radiographic COVID-19 detection selects shortcuts over signal

Alex J. DeGrave*, Joseph D. Janizek*, Su-In Lee 

Nature Machine Intelligence  3, 610–619 

pdf | medRxiv

 A    J   Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Gabriel Erion*, Joseph D. Janizek*, Pascal Sturmfels*, Scott Lundberg, Su-In Lee 

Nature Machine Intelligence  3, 620–631

pdf | arXiv 

 A    J   Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Joseph D. Janizek*, Pascal Sturmfels*, Su-In Lee

Journal of Machine Learning Research  22 (2021) 1-54 

pdf | arXiv

 A    J   Explaining by Removing: A Unified Framework for Model Explanation

Ian Covert, Scott Lundberg, Su-In Lee

Journal of Machine Learning Research 22 (2021) 1-90  

pdf | a short version arXiv  

 C    J   Efficient and explainable risk assessments for imminent dementia: insights from an aging cohort study

Nicasia Beebe-Wang*, Alex Okeson*, Tim Althoff**, Su-In Lee**

IEEE J Biomed Health Inform. 2021 Feb 17; PP. doi: 10.1109/JBHI.2021.3059563


 A   C   Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

Ian Covert, Su-In Lee

International Conference on AI & Statistics (AISTATS) 2021  



 A   C   Learning Deep Attribution Priors Based On Prior Knowledge  

Ethan Weinberger, Joseph Janizek, Su-In Lee 

Neural Information Processing Systems (NeurIPS) 2020  

pdf | arXiv 

 A   C   Understanding Global Feature Contributions through Additive Importance Measures

Ian Covert, Scott Lundberg, Su-In Lee 

Neural Information Processing Systems (NeurIPS) 2020  

pdf | arXiv

 A    J   From Local Explanations to Global Understanding with Explainable AI for Trees  

Scott M. Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee

Nature Machine Intelligence  2, 56–67 (Jan 2020)  Featured on the Cover


 A    J   Visualizing the Impact of Feature Attribution Baselines

Pascal Sturmfels, Scott M. Lundberg, and Su-In Lee

Distill  5 (1), e22  10.23915/distill.00021 


 B   C   Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

Ayse B. Dincer, Joseph D. Janizek, Su-In Lee

ECCB 2020 (Published in Bioinformatics )


 C   C   An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs

Joseph D. Janizek, Gabriel Erion, Alex J. DeGrave, and Su-In Lee

ACM Conference on Health, Inference, and Learning (CHIL 2020)



 B   C   EXPERT: Explainable Prediction of Transcription Factor Binding based on Histone Modification Data

William Chen, Joseph D. Janizek, Su-In. Lee

Highlighted as a spotlight talk at MLCB 


 B    J   AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification

Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee

Nucleic Acids Research  (June 2019) vol. 47, issue 10, page e58 

pdf | GitHub  


 B    J   Associations Between Genetic Data and Quantitative Assessment of Normal Facial Asymmetry

Sara Rolfe, Su-In Lee, Linda Shapiro

Frontiers in Genetics, 18 December 2018 


 C    J   Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

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 Kim, Su-In Lee

Nature Biomedical Engineering 2, 749–760 (Oct 2018) - Featured on the Cover


Allen School News - “Prescience” interpretable machine-learning system for predicting complications during surgery featured in Nature Biomedical Engineering

 B    J   A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

Su-In Lee*,C, Safiye 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, Pamela S. Becker

Nature Communications 9, Article number: 42  2018 

pdf | project website

 B   C   High Throughput Drug Screening of Leukemia Stem Cells Reveals Resistance to Standard Therapies and Sensitivity to Other Agents in Acute Myeloid Leukemia

Frances Linzee Mabrey, Sylvia S Chien, Timothy S Martins, James Annis, Taylor S Sekizaki, Jin Dai, Robert A. Beckman, Lawrence A. Loeb, Andrew Carson, Bradley Patay, Carl Anthony Blau, Vivian G. Oehler, Safiye S Celik, Su-In Lee, Raymond J. Monnat, Janis L. Abkowitz, Frederick R. Appelbaum, Elihu H. Estey and Pamela S Becker

Blood 2018 132:180

 B   C   Identifying progressive gene network perturbation from single-cell RNA-seq data

Sumit Mukherjee, Alberto Carignano, Georg Seelig*, Su-In Lee*

IEEE Engineering in Medicine and Biology Society (EMBC) 2018.  



 A   C   A unified approach to interpreting model predictions

Scott M. Lundberg, Su-In Lee

Neural Information Processing Systems (NeurIPS) 2017  Oral Presentation 

pdf | GitHub


 B    J   Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara H. Rendi, David R. Hawkins, Su-In Lee

Genome Medicine  2016 Jun 10;8(1):66.  

pdf | project website

 B    J   ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, Su-In Lee  Genome Biology  2016 Apr 30;17(1):82.  

pdf | project website 

 B    J   Identifying Network Perturbation in Cancer

Maxim Grechkin, Benjamin A. Logsdon, Andrew J. Gentles, Su-In Lee

PLOS Computational Biology 2016 12(5): e1004888.  

pdf | project website 

 B    J   A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple Negative Breast Cancer

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, Jingchun Zhu

Journal of the National Comprehensive Cancer Network.2016 Jan;14(1):8 - 17.


 A   C   Learning Sparse Gaussian Graphical Models with Overlapping Blocks

Javad Hosseini, Su-In Lee

Neural Information Processing Systems (NeurIPS) 2016.  

pdf | project website

 A   C   Cloud Control: Leveraging many public ChIP-seq control experiments to better remove background noise

Naozumi Hiranuma, Scott Lundberg, Su-In Lee. 

The 7th ACM Conference on Bioinformatics, Computational Biology (ACM-BCB) 2016.  



 B    J   The Proteomic Landscape of Triple-Negative Breast Cancer

Robert T. Lawrence, Elizabeth M. Perez, Daniel Hernández, Chris P. Miller, Kelsey M. Haas, Hanna Y. Irie, Su-In Lee, C. Anthony Blau, Judit Villén 

Cell Reports Volume 11, Issue 4, p630–644, 28.  


 B    J   Sparse expression bases in cancer reveal tumor drivers

Benjamin A. Logsdon, Andrew J. Gentles, Chris P. Miller, C. Anthony Blau, Pamela S. Becker, Su-In Lee

Nucleic Acids Research 10.1093/nar/gku1290.  

pdf | project website

 A   C   Pathway Graphical Lasso

Maxim Grechkin, Maryam Fazel, Daniela Witten, Su-In Lee

AAAI Conference on Artificial Intelligence (AAAI'15)

 pdf | project website


 A    J   Node-Based Learning of Multiple Gaussian Graphical Models

Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee

Journal of Machine Learning Research (JMLR) 15(Feb):445 - 488.  

pdf | project website

 A    J   Learning Graphical Models With Hubs

Kean Ming Tan, Palma London, Su-In Lee, Maryam Fazel, Daniela Witten

Journal of Machine Learning Research (JMLR) 15(2014):3297- 3331.  

pdf | project website

 A   C   Efficient Dimensionality Reduction for High-Dimensional Network Estimation

Safiye Celik, Benjamin Logsdon, Su-In Lee 

International Conference on Machine Learning (ICML)

pdf | project website


 C   C   The Use of Pseudo-Landmarks for Craniofacial Analysis: A Comparative Study with L1-Regularized Logistic Regression

Ezgi Mercan, Linda Shapiro, Seth Weinberg, Su-In Lee

IEEE Engineering in Medicine and Biology Society (EMBC) PMID: 24111127



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


 B    J   Massively parallel functional dissection of mammalian enhancers in vivo

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*, Jay Shendure* 

Nature Biotechnology, 30(3), 265-70.  


 A   C   Structured Learning of Gaussian Graphical Models

Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-In Lee, Maryam Fazel

Neural Information Processing Systems (NeurIPS)  

pdf | project website

 C   C   Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models  

Shulin Yang, Linda Shapiro, Michael Cunningham, Matthew Speltz, Craig Birgfeld, Indriyati Atmosukarto, Su-In Lee

Medical Image Computing and Computer Assisted Intervention (MICCAI)  Best paper award


 C   C   Tremor Detection Using Motion Filter and SVM

Bilge Soran, Jeng-Neng Hwang, Su-In Lee, Linda Shapiro

International Conference on Pattern Recognition.  


 C   C   Parcellation of Human Inferior Parietal Lobule Based On Diffusion MRI

Bilge Soran, Zhiyong Xie, Rosalia Tungaraza, Su-In Lee, Linda Shapiro, Thomas Grabowski

IEEE Engineering in Medicine & Biology Society, Engineering Innovation in Global Health  PMID: 23366611  



 B    J   Brn3a and Islet1 act epistatically to regulate the gene expression program of sensory differentiation

Iain M. Dykes, Lynne Tempest, Su-In Lee, Eric E. Turner

Journal of Neuroscience, 31(27), 9789-99

pdf | project website

 B    J   Learning Generative Models for Protein Fold Families

Sivaraman Balakrishnan, Hetunandan Kamisetty, Jaime G. Carbonell, Su-In Lee, Christopher J. Langmead

PROTEINS: Structure, Function, and Bioinformatics, 79(4), 1061-78

pdf | project website

 C   C   Classification and Interest Region Localization on Craniosynostosis Skulls

Shulin Yang, Linda Shapiro, Michael L. Cunningham, Matthew Speltz, Su-In Lee

ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB)


2010 and before

 B   C   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.

 B    J   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.  [Paper]

 B    J   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]

 A   C   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%  [Paper

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

 A   C   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]

 B    J   Su-In Lee*, Dana Pe’er*, 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.[Paper

 B    J   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.  [Paper]

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

 B    J   S.-I. Lee, and S. Batzoglou (2003). Application of Independent Component Analysis to Microarrays.  Genome Biology, 4(11), R76.  [Paper]

 B    J   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)  [Link]

 A   C    S.-I. Lee, and S.-Y. Lee (2000). Top-Down Attention Control at Feature Selection.  Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision (BMCV).[Bibtex]