A AI/ML
B Basic Biology
C Clinical Medicine
Embedded Files
J Journal publications
C Conference publications
P Perspectives & Reviews
* indicates equal contribution
Preprints
Preprints
B J A deep generative model of single-cell methylomic data
Ethan Weinberger, Wei Qiu, Wei Tian, Qiurui Zeng, Martin Kim, Can Ergen, Nir Yosef, Joseph R. Ecker, and Su-In Lee
Under Review in Nature Genetics
bioRxiv
B C Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI
Wei Qiu, Ethan Weinberger, Su-In Lee
C C LIFT - XAI: Leveraging Important Features in Treatment Effects to Inform Clinical Decision-Making via Explainable AI
Mingyu Lu, Ian Covert, Nathan J. White,* and 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 (from the lab)
Selected Publications (from the lab)
2025
2025
A C Efficient Shapley Values for Attributing Global Properties of Diffusion Models to Data Group
Chris Lin,* Mingyu Lu,* Chanwoo Kim, and Su-In Lee
International Conference on Representation Learning (2025)
2024
2024
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*
Nature Biomedical Engineering (2024)
News & Views in Nature BME: https://www.nature.com/articles/s41551-024-01322-3
https://www.nature.com/articles/s41551-024-01290-8
C C Discovering mechanisms underlying AI prediction of protected attributes via data auditing
Soham Gadgil,* Alex J. DeGrave,* Roxana Daneshjou, and Su-In Lee
Won the best paper runner-up award at the Data Curation and Augmentation in Medical Imaging Workshop at CVPR (2024)
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*
Nature Medicine
Featured in Allen School News
A C Estimating Conditional Mutual Information for Dynamic Feature Selection
Soham U. Gadgil*, Ian Covert*, Su-In Lee
International Conference on Learning Representations (ICLR)
2023
2023
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
Featured in Allen School News
Featured in News & Views, Lang et al. "Explaining counterfactual images"Nature Biomedical Engineering (2023)
A C Feature selection in the contrastive analysis setting
Ethan Weinberger, Ian Covert, Su-In Lee
Neural Information Processing Systems (NeurIPS) 2023
arXiv
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
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)
A J Algorithms to estimate Shapley value feature attributions
Hugh Chen*, Ian Covert*, Scott Lundberg, Su-In Lee
Nature Machine Intelligence (2023)
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)
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)
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
A C Learning to estimate Shapley values with vision transformers
Ian Covert*, Chanwoo Kim*, Su-In Lee
International Conference on Learning Representations (ICLR) spotlight
A C Contrastive Corpus Attribution for Explaining Representations
Chris Lin*, Hugh Chen*, Chanwoo Kim, Su-In Lee
International Conference on Learning Representations (ICLR)
2022
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
C P Breaking into the black box of artificial intelligence
Nature Outlook written by Neil Savage
Featuring DeGrave*, Joseph D. Janizek*, and Su-In Lee. "AI for radiographic COVID-19 detection selects shortcuts over signal." Nature Machine Intelligence 2021
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
Research Highlight, Nature Computational Science
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
2021
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
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)
Multi-task deep learning for Alzheimer’s disease neuropathology. Highlighted as a spotlight talk at MLCB; ICML Workshop on Computational Biology.
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
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
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
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
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
2020
2020
A C Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Neural Information Processing Systems (NeurIPS) 2020
A C Understanding Global Feature Contributions through Additive Importance Measures
Ian Covert, Scott Lundberg, Su-In Lee
Neural Information Processing Systems (NeurIPS) 2020
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
Allen School News - Seeing the forest for the trees: UW team advances explainable AI for popular machine learning models used to predict human disease and mortality risks.
Nature NI News and Views - Learning with explainable trees
Preprint - Scott M. Lundberg, Gabriel G. Erion, Su-In Lee, Consistent individualized feature attribution for tree ensembles
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
A C True to the Model or True to the Data?
Hugh Chen, Joseph D. Janizek, Scott Lundberg, and Su-In Lee
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
2019
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
Allen School News - With AIControl, Allen School researchers replace biological experiments with AI to better understand the human genome
2018
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
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
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
Recommended in F1000Prime as being of special significance in its field:
GeekWire - Using precision medicine to kill cancer — with artificial intelligence
Allen School News - Researchers “MERGE” machine learning and medicine to enable targeted treatment of individual cancer patients
UW Huddle - Personal beginnings for a personalized medicine breakthrough
1 year, 10 innovations from UW's Paul G. Allen School of CSE
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.
2017
2017
A C A unified approach to interpreting model predictions
Scott M. Lundberg, Su-In Lee
Neural Information Processing Systems (NeurIPS) 2017 Oral Presentation
[3 min video] [NeurIPS 2017 Oral (17:45)]
Selected for oral presentation (acceptance rate: 1.2%)
2016
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.
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.
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.
A C Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Javad Hosseini, Su-In Lee
Neural Information Processing Systems (NeurIPS) 2016.
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.
2015
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.
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.
A C Pathway Graphical Lasso
Maxim Grechkin, Maryam Fazel, Daniela Witten, Su-In Lee
AAAI Conference on Artificial Intelligence (AAAI'15)
2014
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.
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.
A C Efficient Dimensionality Reduction for High-Dimensional Network Estimation
Safiye Celik, Benjamin Logsdon, Su-In Lee
International Conference on Machine Learning (ICML).
2013
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
2012
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.
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)
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
2011
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
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
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
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]
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