A AI/ML

B Basic biology

C Clinical medicine & healthcare

J Journal publications

C Conference publications


* indicates equal contribution

Preprints


A J Algorithms to estimate Shapley value feature attributions

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

Nature Machine Intelligence (minor revision; accepted in principle)

arXiv


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 (major revision)

bioRxiv


B J 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 (major revision)

bioRxiv


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

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

Nature Methods (major revision)

bioRxiv


C J An explainable AI framework for interpretable biological age

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

Lancet Healthy Longevity (revision)

medRxiv


C C A deep Bayesian bandits approach for personalized anticancer therapy

Mingyu Lu, Yifang Chen, Su-In Lee

arXiv


B J Transferable representations of single-cell transcriptomic data

Ethan Weinberger, Su-In Lee.

bioRxiv

Selected Publications


2023


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


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 (in press)

bioRxiv



2022


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 J Breaking into the black box of artificial intelligence

Nature Outlook written by Neil Savage

pdf

  • Featuring DeGrave*, Joseph D. Janizek*, and Su-In Lee. "AI for radiographic COVID-19 detection selects shortcuts over signal." Nature Machine Intelligence 2021

pdf | medRxiv


C 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)

pdf


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

pdf


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

  • Research Highlight, Nature Computational Science

pdf


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

arXiv


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

pdf



2021


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

pdf


C J Course corrections for clinical AI

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

Kidney360 10.34067/KID.0004152021, September 2021

pdf


B J 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

pdf


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)

pdf

  • Multi-task deep learning for Alzheimer’s disease neuropathology. Highlighted as a spotlight talk at MLCB; ICML Workshop on Computational Biology.

bioRxiv


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

pdf


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

pdf


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

Ian Covert, Su-In Lee

International Conference on AI & Statistics (AISTATS) 2021

arXiv



2020


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

pdf


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

pdf


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 )

pdf


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)

pdf

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



2019


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

  • Allen School News - With AIControl, Allen School researchers replace biological experiments with AI to better understand the human genome



2018


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

pdf


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

pdf

  • Nature BME Editorial - Towards trustable machine learning

  • UW News - Prescience: Helping doctors predict the future

  • GeekWire - Univ. of Washington researchers unveil Prescience, an AI system that predicts problems during surgery

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.

bioRxiv


2017

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



2016


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

  • Featured in Casey S. Greene. The future is unsupervised. Science Translational Medicine 06 Jul 2016, Vol. 8 (346), pp. 346ec108 Editors' Choice [Link]


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

  • Recommended in F1000Prime as being of special significance in its field


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.

pdf


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.

pdf



2015

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.

pdf


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



2014

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 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


2013

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

pdf



2012


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.

pdf


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.

pdf


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

pdf


C C Tremor Detection Using Motion Filter and SVM

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

International Conference on Pattern Recognition.

pdf


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

pdf



2011

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).

pdf



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]