Su-In Lee
Su-In Lee
Computer Science & Engineering, University of Washington
Verified email at - Homepage
Cited by
Cited by
A unified approach to interpreting model predictions
S Lundberg, SI Lee
arXiv preprint arXiv:1705.07874, 2017
Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae
JE Galagan, SE Calvo, C Cuomo, LJ Ma, JR Wortman, S Batzoglou, ...
Nature 438 (7071), 1105-1115, 2005
From local explanations to global understanding with explainable AI for trees
SM Lundberg, G Erion, H Chen, A DeGrave, JM Prutkin, B Nair, R Katz, ...
Nature machine intelligence 2 (1), 56-67, 2020
Massively parallel functional dissection of mammalian enhancers in vivo
RP Patwardhan, JB Hiatt, DM Witten, MJ Kim, RP Smith, D May, C Lee, ...
Nature biotechnology 30 (3), 265, 2012
Efficient l~ 1 regularized logistic regression
SI Lee, H Lee, P Abbeel, AY Ng
Aaai 6, 401-408, 2006
Consistent individualized feature attribution for tree ensembles
SM Lundberg, GG Erion, SI Lee
arXiv preprint arXiv:1802.03888, 2018
Application of independent component analysis to microarrays
SI Lee, S Batzoglou
Genome biology 4 (11), 1-21, 2003
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
SM Lundberg, B Nair, MS Vavilala, M Horibe, MJ Eisses, T Adams, ...
Nature biomedical engineering 2 (10), 749-760, 2018
Efficient Structure Learning of Markov Networks using -Regularization
SI Lee, V Ganapathi, D Koller
Advances in neural Information processing systems, 2006
Learning generative models for protein fold families
S Balakrishnan, H Kamisetty, JG Carbonell, SI Lee, CJ Langmead
Proteins: Structure, Function, and Bioinformatics 79 (4), 1061-1078, 2011
Learning a prior on regulatory potential from eQTL data
SI Lee, AM Dudley, D Drubin, PA Silver, NJ Krogan, D Pe'er, D Koller
PLoS Genet 5 (1), e1000358, 2009
Learning a meta-level prior for feature relevance from multiple related tasks
SI Lee, V Chatalbashev, D Vickrey, D Koller
Proceedings of the 24th international conference on Machine learning, 489-496, 2007
The proteomic landscape of triple-negative breast cancer
RT Lawrence, EM Perez, D Hernández, CP Miller, KM Haas, HY Irie, ...
Cell reports 11 (4), 630-644, 2015
Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification
SI Lee, D Pe'er, AM Dudley, GM Church, D Koller
Proceedings of the National Academy of Sciences 103 (38), 14062-14067, 2006
Node-based learning of multiple gaussian graphical models
K Mohan, P London, M Fazel, D Witten, SI Lee
The Journal of Machine Learning Research 15 (1), 445-488, 2014
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
SI Lee, S Celik, BA Logsdon, SM Lundberg, TJ Martins, VG Oehler, ...
Nature communications 9 (1), 1-13, 2018
Explainable AI for trees: From local explanations to global understanding
SM Lundberg, G Erion, H Chen, A DeGrave, JM Prutkin, B Nair, R Katz, ...
arXiv preprint arXiv:1905.04610, 2019
Brn3a and Islet1 act epistatically to regulate the gene expression program of sensory differentiation
IM Dykes, L Tempest, SI Lee, EE Turner
Journal of Neuroscience 31 (27), 9789-9799, 2011
Learning graphical models with hubs
KM Tan, P London, K Mohan, SI Lee, M Fazel, D Witten
arXiv preprint arXiv:1402.7349, 2014
An unexpected unity among methods for interpreting model predictions
S Lundberg, SI Lee
arXiv preprint arXiv:1611.07478, 2016
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